Overview

Dataset statistics

Number of variables46
Number of observations12192
Missing cells205935
Missing cells (%)36.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.5 MiB
Average record size in memory8.5 KiB

Variable types

Text36
Numeric4
URL1
Categorical5

Alerts

Source has constant value "Scopus"Constant
Conference code is highly overall correlated with PubMed ID and 2 other fieldsHigh correlation
PubMed ID is highly overall correlated with Conference code and 1 other fieldsHigh correlation
Publication Stage is highly overall correlated with Conference codeHigh correlation
Year is highly overall correlated with Conference code and 1 other fieldsHigh correlation
Language of Original Document is highly imbalanced (92.6%)Imbalance
Publication Stage is highly imbalanced (92.2%)Imbalance
Authors has 384 (3.1%) missing valuesMissing
Author full names has 384 (3.1%) missing valuesMissing
Author(s) ID has 384 (3.1%) missing valuesMissing
Source title has 3321 (27.2%) missing valuesMissing
Volume has 4208 (34.5%) missing valuesMissing
Issue has 7629 (62.6%) missing valuesMissing
Art. No. has 9589 (78.6%) missing valuesMissing
Page start has 3148 (25.8%) missing valuesMissing
Page end has 3305 (27.1%) missing valuesMissing
DOI has 2068 (17.0%) missing valuesMissing
Affiliations has 558 (4.6%) missing valuesMissing
Authors with affiliations has 384 (3.1%) missing valuesMissing
Author Keywords has 2918 (23.9%) missing valuesMissing
Index Keywords has 4826 (39.6%) missing valuesMissing
Molecular Sequence Numbers has 12188 (> 99.9%) missing valuesMissing
Chemicals/CAS has 12059 (98.9%) missing valuesMissing
Tradenames has 12174 (99.9%) missing valuesMissing
Manufacturers has 12183 (99.9%) missing valuesMissing
Funding Details has 8305 (68.1%) missing valuesMissing
Funding Texts has 8043 (66.0%) missing valuesMissing
References has 740 (6.1%) missing valuesMissing
Correspondence Address has 5302 (43.5%) missing valuesMissing
Editors has 9435 (77.4%) missing valuesMissing
Publisher has 1121 (9.2%) missing valuesMissing
Sponsors has 9667 (79.3%) missing valuesMissing
Conference name has 6503 (53.3%) missing valuesMissing
Conference date has 7267 (59.6%) missing valuesMissing
Conference location has 7265 (59.6%) missing valuesMissing
Conference code has 7267 (59.6%) missing valuesMissing
ISSN has 4939 (40.5%) missing valuesMissing
ISBN has 5604 (46.0%) missing valuesMissing
CODEN has 10506 (86.2%) missing valuesMissing
PubMed ID has 11528 (94.6%) missing valuesMissing
Open Access has 8657 (71.0%) missing valuesMissing
doi_norm has 2068 (17.0%) missing valuesMissing
Cited by is highly skewed (γ1 = 49.58005625)Skewed
Link has unique valuesUnique
EID has unique valuesUnique
Cited by has 3335 (27.4%) zerosZeros

Reproduction

Analysis started2026-01-14 11:02:20.762980
Analysis finished2026-01-14 11:02:48.602537
Duration27.84 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Authors
Text

Missing 

Distinct10868
Distinct (%)92.0%
Missing384
Missing (%)3.1%
Memory size1.2 MiB
2026-01-14T11:02:48.987874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length620
Median length234
Mean length41.251355
Min length6

Characters and Unicode

Total characters487096
Distinct characters152
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10225 ?
Unique (%)86.6%

Sample

1st rowWang, Y.
2nd rowLin, Y.; Zhang, Y.; Yang, Y.; Pan, S.; Ren, X.; Chen, D.
3rd rowHsu, T.-C.; Hsu, T.-P.
4th rowAksoy, B.D.; Mumcu, F.K.; Cantürk Günhan, B.C.
5th rowvan Bergen, R.; Huebotter, J.; A.; Lanillos, P.
ValueCountFrequency (%)
m2633
 
3.4%
a2378
 
3.0%
s2225
 
2.8%
j2215
 
2.8%
c1461
 
1.9%
d1323
 
1.7%
r1252
 
1.6%
l1173
 
1.5%
y1164
 
1.5%
k1048
 
1.3%
Other values (17825)61208
78.4%
2026-01-14T11:02:49.554286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66270
 
13.6%
.50065
 
10.3%
,38180
 
7.8%
a29872
 
6.1%
;26442
 
5.4%
e20717
 
4.3%
n19571
 
4.0%
i17642
 
3.6%
o16140
 
3.3%
r16098
 
3.3%
Other values (142)186099
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)487096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
66270
 
13.6%
.50065
 
10.3%
,38180
 
7.8%
a29872
 
6.1%
;26442
 
5.4%
e20717
 
4.3%
n19571
 
4.0%
i17642
 
3.6%
o16140
 
3.3%
r16098
 
3.3%
Other values (142)186099
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)487096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
66270
 
13.6%
.50065
 
10.3%
,38180
 
7.8%
a29872
 
6.1%
;26442
 
5.4%
e20717
 
4.3%
n19571
 
4.0%
i17642
 
3.6%
o16140
 
3.3%
r16098
 
3.3%
Other values (142)186099
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)487096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
66270
 
13.6%
.50065
 
10.3%
,38180
 
7.8%
a29872
 
6.1%
;26442
 
5.4%
e20717
 
4.3%
n19571
 
4.0%
i17642
 
3.6%
o16140
 
3.3%
r16098
 
3.3%
Other values (142)186099
38.2%

Author full names
Text

Missing 

Distinct10811
Distinct (%)91.6%
Missing384
Missing (%)3.1%
Memory size2.1 MiB
2026-01-14T11:02:50.128553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1511
Median length447
Mean length102.36247
Min length19

Characters and Unicode

Total characters1208696
Distinct characters178
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10136 ?
Unique (%)85.8%

Sample

1st rowWang, Yang (57208730125)
2nd rowLin, Yuru (57281795200); Zhang, Yi (58957195500); Yang, Yuqin (57164390600); Pan, Shidan (60209651800); Ren, Xu (60209651900); Chen, Dengkang (57898076100)
3rd rowHsu, Tingchia (35173046500); Hsu, Taiping (58366049000)
4th rowAksoy, Behiye Dinçer (60177502400); Mumcu, Filiz Kuşkaya (13410584100); Cantürk Günhan, Berna (36815607700)
5th rowvan Bergen, Ruben S. (55502596000); Huebotter, Justus F. (57901993200); Lanillos, Pablo (24076529300)
ValueCountFrequency (%)
a887
 
0.7%
m872
 
0.7%
j710
 
0.5%
wang523
 
0.4%
li460
 
0.4%
s458
 
0.3%
c457
 
0.3%
r448
 
0.3%
l431
 
0.3%
zhang430
 
0.3%
Other values (53187)125656
95.7%
2026-01-14T11:02:50.975857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
119520
 
9.9%
082349
 
6.8%
a63480
 
5.3%
556726
 
4.7%
n43018
 
3.6%
742759
 
3.5%
i42653
 
3.5%
e41241
 
3.4%
239625
 
3.3%
(38255
 
3.2%
Other values (168)639070
52.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1208696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
119520
 
9.9%
082349
 
6.8%
a63480
 
5.3%
556726
 
4.7%
n43018
 
3.6%
742759
 
3.5%
i42653
 
3.5%
e41241
 
3.4%
239625
 
3.3%
(38255
 
3.2%
Other values (168)639070
52.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1208696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
119520
 
9.9%
082349
 
6.8%
a63480
 
5.3%
556726
 
4.7%
n43018
 
3.6%
742759
 
3.5%
i42653
 
3.5%
e41241
 
3.4%
239625
 
3.3%
(38255
 
3.2%
Other values (168)639070
52.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1208696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
119520
 
9.9%
082349
 
6.8%
a63480
 
5.3%
556726
 
4.7%
n43018
 
3.6%
742759
 
3.5%
i42653
 
3.5%
e41241
 
3.4%
239625
 
3.3%
(38255
 
3.2%
Other values (168)639070
52.9%

Author(s) ID
Text

Missing 

Distinct10811
Distinct (%)91.6%
Missing384
Missing (%)3.1%
Memory size1.0 MiB
2026-01-14T11:02:51.401672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length604
Median length430
Mean length39.570461
Min length10

Characters and Unicode

Total characters467248
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10136 ?
Unique (%)85.8%

Sample

1st row57208730125
2nd row57281795200; 58957195500; 57164390600; 60209651800; 60209651900; 57898076100
3rd row35173046500; 58366049000
4th row60177502400; 13410584100; 36815607700
5th row55502596000; 57901993200; 60247114700; 24076529300
ValueCountFrequency (%)
650781992050
 
0.1%
963819440050
 
0.1%
3510387060047
 
0.1%
2309666680046
 
0.1%
5721172689045
 
0.1%
670149242342
 
0.1%
3517304650041
 
0.1%
670159412641
 
0.1%
720304480039
 
0.1%
828649600039
 
0.1%
Other values (25736)37810
98.8%
2026-01-14T11:02:52.093718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
082379
17.6%
556733
12.1%
742768
9.2%
239643
8.5%
635999
7.7%
135455
7.6%
931345
 
6.7%
331326
 
6.7%
429614
 
6.3%
829102
 
6.2%
Other values (2)52884
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)467248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
082379
17.6%
556733
12.1%
742768
9.2%
239643
8.5%
635999
7.7%
135455
7.6%
931345
 
6.7%
331326
 
6.7%
429614
 
6.3%
829102
 
6.2%
Other values (2)52884
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)467248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
082379
17.6%
556733
12.1%
742768
9.2%
239643
8.5%
635999
7.7%
135455
7.6%
931345
 
6.7%
331326
 
6.7%
429614
 
6.3%
829102
 
6.2%
Other values (2)52884
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)467248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
082379
17.6%
556733
12.1%
742768
9.2%
239643
8.5%
635999
7.7%
135455
7.6%
931345
 
6.7%
331326
 
6.7%
429614
 
6.3%
829102
 
6.2%
Other values (2)52884
11.3%

Title
Text

Distinct12076
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2026-01-14T11:02:52.572268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length476
Median length263
Mean length92.691929
Min length6

Characters and Unicode

Total characters1130100
Distinct characters746
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11997 ?
Unique (%)98.4%

Sample

1st rowEffects of troubleshooting robotics learning on students’ engagement, computational thinking, and programming skills
2nd rowFacilitating computational thinking with AI: A three-level meta-analytic evidence for future-ready learning
3rd rowEffects of game-based learning integrated with different thinking-guided methods on computational thinking of elementary school students
4th rowUnveiling the nexus: Computational thinking and mathematical modelling in K-12 education- a teacher-centric exploration
5th rowObject-centric proto-symbolic behavioural reasoning from pixels
ValueCountFrequency (%)
of5923
 
4.1%
and5369
 
3.7%
in4968
 
3.4%
computational4947
 
3.4%
thinking4866
 
3.3%
the4180
 
2.9%
a3874
 
2.7%
for2934
 
2.0%
to2151
 
1.5%
on1951
 
1.3%
Other values (14201)104680
71.8%
2026-01-14T11:02:53.318240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
133418
 
11.8%
n89717
 
7.9%
i88928
 
7.9%
e85590
 
7.6%
t74627
 
6.6%
o74583
 
6.6%
a74075
 
6.6%
r51114
 
4.5%
s45739
 
4.0%
l38884
 
3.4%
Other values (736)373425
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1130100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
133418
 
11.8%
n89717
 
7.9%
i88928
 
7.9%
e85590
 
7.6%
t74627
 
6.6%
o74583
 
6.6%
a74075
 
6.6%
r51114
 
4.5%
s45739
 
4.0%
l38884
 
3.4%
Other values (736)373425
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1130100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
133418
 
11.8%
n89717
 
7.9%
i88928
 
7.9%
e85590
 
7.6%
t74627
 
6.6%
o74583
 
6.6%
a74075
 
6.6%
r51114
 
4.5%
s45739
 
4.0%
l38884
 
3.4%
Other values (736)373425
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1130100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
133418
 
11.8%
n89717
 
7.9%
i88928
 
7.9%
e85590
 
7.6%
t74627
 
6.6%
o74583
 
6.6%
a74075
 
6.6%
r51114
 
4.5%
s45739
 
4.0%
l38884
 
3.4%
Other values (736)373425
33.0%

Year
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.948
Minimum1970
Maximum2026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2026-01-14T11:02:53.531123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile2006
Q12017
median2021
Q32023
95-th percentile2025
Maximum2026
Range56
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.3713909
Coefficient of variation (CV)0.0031557974
Kurtosis5.4101335
Mean2018.948
Median Absolute Deviation (MAD)3
Skewness-2.0310781
Sum24615014
Variance40.594622
MonotonicityDecreasing
2026-01-14T11:02:53.771886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20241445
11.9%
20251331
10.9%
20231289
10.6%
20221129
9.3%
20211056
8.7%
2020974
 
8.0%
2019868
 
7.1%
2018667
 
5.5%
2017560
 
4.6%
2016384
 
3.1%
Other values (40)2489
20.4%
ValueCountFrequency (%)
19701
 
< 0.1%
19751
 
< 0.1%
19771
 
< 0.1%
19801
 
< 0.1%
19811
 
< 0.1%
19821
 
< 0.1%
19835
< 0.1%
19847
0.1%
19851
 
< 0.1%
19865
< 0.1%
ValueCountFrequency (%)
202668
 
0.6%
20251331
10.9%
20241445
11.9%
20231289
10.6%
20221129
9.3%
20211056
8.7%
2020974
8.0%
2019868
7.1%
2018667
5.5%
2017560
 
4.6%

Source title
Text

Missing 

Distinct2267
Distinct (%)25.6%
Missing3321
Missing (%)27.2%
Memory size884.8 KiB
2026-01-14T11:02:54.085599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length158
Median length93
Mean length41.140458
Min length3

Characters and Unicode

Total characters364957
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1424 ?
Unique (%)16.1%

Sample

1st rowThinking Skills and Creativity
2nd rowThinking Skills and Creativity
3rd rowThinking Skills and Creativity
4th rowThinking Skills and Creativity
5th rowNeural Networks
ValueCountFrequency (%)
of2817
 
6.2%
and2580
 
5.7%
in2237
 
4.9%
conference2159
 
4.8%
education2127
 
4.7%
journal1765
 
3.9%
international1660
 
3.7%
proceedings1526
 
3.4%
science1376
 
3.0%
computer1106
 
2.4%
Other values (1847)26001
57.3%
2026-01-14T11:02:56.467286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n38658
 
10.6%
36483
 
10.0%
e34778
 
9.5%
o28604
 
7.8%
i25853
 
7.1%
a21849
 
6.0%
t19117
 
5.2%
r18414
 
5.0%
c18142
 
5.0%
s11549
 
3.2%
Other values (59)111510
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)364957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n38658
 
10.6%
36483
 
10.0%
e34778
 
9.5%
o28604
 
7.8%
i25853
 
7.1%
a21849
 
6.0%
t19117
 
5.2%
r18414
 
5.0%
c18142
 
5.0%
s11549
 
3.2%
Other values (59)111510
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)364957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n38658
 
10.6%
36483
 
10.0%
e34778
 
9.5%
o28604
 
7.8%
i25853
 
7.1%
a21849
 
6.0%
t19117
 
5.2%
r18414
 
5.0%
c18142
 
5.0%
s11549
 
3.2%
Other values (59)111510
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)364957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n38658
 
10.6%
36483
 
10.0%
e34778
 
9.5%
o28604
 
7.8%
i25853
 
7.1%
a21849
 
6.0%
t19117
 
5.2%
r18414
 
5.0%
c18142
 
5.0%
s11549
 
3.2%
Other values (59)111510
30.6%

Volume
Text

Missing 

Distinct1338
Distinct (%)16.8%
Missing4208
Missing (%)34.5%
Memory size540.2 KiB
2026-01-14T11:02:56.792037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length69
Median length2
Mean length3.3999248
Min length1

Characters and Unicode

Total characters27145
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique809 ?
Unique (%)10.1%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row197
ValueCountFrequency (%)
2310
 
3.5%
lncs306
 
3.5%
1284
 
3.2%
13182
 
2.1%
14164
 
1.9%
15162
 
1.9%
12154
 
1.8%
10153
 
1.7%
11140
 
1.6%
29138
 
1.6%
Other values (1313)6755
77.2%
2026-01-14T11:02:57.245029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14161
15.3%
23525
13.0%
32023
 
7.5%
01832
 
6.7%
41542
 
5.7%
51506
 
5.5%
61276
 
4.7%
91248
 
4.6%
81247
 
4.6%
71122
 
4.1%
Other values (44)7663
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)27145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14161
15.3%
23525
13.0%
32023
 
7.5%
01832
 
6.7%
41542
 
5.7%
51506
 
5.5%
61276
 
4.7%
91248
 
4.6%
81247
 
4.6%
71122
 
4.1%
Other values (44)7663
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)27145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14161
15.3%
23525
13.0%
32023
 
7.5%
01832
 
6.7%
41542
 
5.7%
51506
 
5.5%
61276
 
4.7%
91248
 
4.6%
81247
 
4.6%
71122
 
4.1%
Other values (44)7663
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)27145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14161
15.3%
23525
13.0%
32023
 
7.5%
01832
 
6.7%
41542
 
5.7%
51506
 
5.5%
61276
 
4.7%
91248
 
4.6%
81247
 
4.6%
71122
 
4.1%
Other values (44)7663
28.2%

Issue
Text

Missing 

Distinct250
Distinct (%)5.5%
Missing7629
Missing (%)62.6%
Memory size463.9 KiB
2026-01-14T11:02:57.459507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length81
Median length1
Mean length1.5844839
Min length1

Characters and Unicode

Total characters7230
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)3.5%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2
ValueCountFrequency (%)
1972
20.5%
2747
15.7%
3581
12.2%
4545
11.5%
6273
 
5.8%
5261
 
5.5%
7117
 
2.5%
9115
 
2.4%
8106
 
2.2%
1188
 
1.9%
Other values (241)939
19.8%
2026-01-14T11:02:57.830584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11591
22.0%
21080
14.9%
3713
9.9%
4639
 
8.8%
6368
 
5.1%
5362
 
5.0%
8204
 
2.8%
7196
 
2.7%
181
 
2.5%
9160
 
2.2%
Other values (52)1736
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)7230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11591
22.0%
21080
14.9%
3713
9.9%
4639
 
8.8%
6368
 
5.1%
5362
 
5.0%
8204
 
2.8%
7196
 
2.7%
181
 
2.5%
9160
 
2.2%
Other values (52)1736
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11591
22.0%
21080
14.9%
3713
9.9%
4639
 
8.8%
6368
 
5.1%
5362
 
5.0%
8204
 
2.8%
7196
 
2.7%
181
 
2.5%
9160
 
2.2%
Other values (52)1736
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11591
22.0%
21080
14.9%
3713
9.9%
4639
 
8.8%
6368
 
5.1%
5362
 
5.0%
8204
 
2.8%
7196
 
2.7%
181
 
2.5%
9160
 
2.2%
Other values (52)1736
24.0%

Art. No.
Text

Missing 

Distinct2309
Distinct (%)88.7%
Missing9589
Missing (%)78.6%
Memory size438.6 KiB
2026-01-14T11:02:58.226791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length18
Mean length5.6131387
Min length1

Characters and Unicode

Total characters14611
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2183 ?
Unique (%)83.9%

Sample

1st row102068
2nd row102070
3rd row102056
4th row102049
5th row108407
ValueCountFrequency (%)
1111
 
0.4%
710
 
0.4%
110
 
0.4%
310
 
0.4%
410
 
0.4%
109
 
0.3%
69
 
0.3%
198
 
0.3%
28
 
0.3%
137
 
0.3%
Other values (2302)2515
96.5%
2026-01-14T11:02:58.774490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
02192
15.0%
12142
14.7%
21493
10.2%
31281
8.8%
41261
8.6%
51200
8.2%
91170
8.0%
71137
7.8%
81128
7.7%
61121
7.7%
Other values (46)486
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02192
15.0%
12142
14.7%
21493
10.2%
31281
8.8%
41261
8.6%
51200
8.2%
91170
8.0%
71137
7.8%
81128
7.7%
61121
7.7%
Other values (46)486
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02192
15.0%
12142
14.7%
21493
10.2%
31281
8.8%
41261
8.6%
51200
8.2%
91170
8.0%
71137
7.8%
81128
7.7%
61121
7.7%
Other values (46)486
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02192
15.0%
12142
14.7%
21493
10.2%
31281
8.8%
41261
8.6%
51200
8.2%
91170
8.0%
71137
7.8%
81128
7.7%
61121
7.7%
Other values (46)486
 
3.3%

Page start
Text

Missing 

Distinct2158
Distinct (%)23.9%
Missing3148
Missing (%)25.8%
Memory size556.4 KiB
2026-01-14T11:02:59.221344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length3
Mean length2.841663
Min length1

Characters and Unicode

Total characters25700
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1129 ?
Unique (%)12.5%

Sample

1st row126
2nd row208
3rd row36
4th row154
5th row213
ValueCountFrequency (%)
1467
 
5.2%
353
 
0.6%
7737
 
0.4%
6334
 
0.4%
5733
 
0.4%
3532
 
0.4%
2132
 
0.4%
11332
 
0.4%
21332
 
0.4%
931
 
0.3%
Other values (2148)8261
91.3%
2026-01-14T11:02:59.756432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15082
19.8%
33110
12.1%
23046
11.9%
52505
9.7%
42286
8.9%
72216
8.6%
92089
8.1%
61973
 
7.7%
81772
 
6.9%
01563
 
6.1%
Other values (18)58
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)25700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15082
19.8%
33110
12.1%
23046
11.9%
52505
9.7%
42286
8.9%
72216
8.6%
92089
8.1%
61973
 
7.7%
81772
 
6.9%
01563
 
6.1%
Other values (18)58
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)25700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15082
19.8%
33110
12.1%
23046
11.9%
52505
9.7%
42286
8.9%
72216
8.6%
92089
8.1%
61973
 
7.7%
81772
 
6.9%
01563
 
6.1%
Other values (18)58
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)25700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15082
19.8%
33110
12.1%
23046
11.9%
52505
9.7%
42286
8.9%
72216
8.6%
92089
8.1%
61973
 
7.7%
81772
 
6.9%
01563
 
6.1%
Other values (18)58
 
0.2%

Page end
Text

Missing 

Distinct2164
Distinct (%)24.4%
Missing3305
Missing (%)27.1%
Memory size554.2 KiB
2026-01-14T11:03:00.248661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9447508
Min length1

Characters and Unicode

Total characters26170
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1119 ?
Unique (%)12.6%

Sample

1st row154
2nd row237
3rd row51
4th row164
5th row240
ValueCountFrequency (%)
1739
 
0.4%
2931
 
0.3%
2729
 
0.3%
2429
 
0.3%
7229
 
0.3%
2829
 
0.3%
7628
 
0.3%
1828
 
0.3%
6228
 
0.3%
7127
 
0.3%
Other values (2154)8590
96.7%
2026-01-14T11:03:00.853460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14413
16.9%
23584
13.7%
32769
10.6%
42653
10.1%
62392
9.1%
52324
8.9%
82091
8.0%
72007
7.7%
01947
7.4%
91938
7.4%
Other values (18)52
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)26170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14413
16.9%
23584
13.7%
32769
10.6%
42653
10.1%
62392
9.1%
52324
8.9%
82091
8.0%
72007
7.7%
01947
7.4%
91938
7.4%
Other values (18)52
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14413
16.9%
23584
13.7%
32769
10.6%
42653
10.1%
62392
9.1%
52324
8.9%
82091
8.0%
72007
7.7%
01947
7.4%
91938
7.4%
Other values (18)52
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14413
16.9%
23584
13.7%
32769
10.6%
42653
10.1%
62392
9.1%
52324
8.9%
82091
8.0%
72007
7.7%
01947
7.4%
91938
7.4%
Other values (18)52
 
0.2%

Cited by
Real number (ℝ)

Skewed  Zeros 

Distinct314
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.663058
Minimum0
Maximum10043
Zeros3335
Zeros (%)27.4%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2026-01-14T11:03:01.009106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q312
95-th percentile68
Maximum10043
Range10043
Interquartile range (IQR)12

Descriptive statistics

Standard deviation128.52238
Coefficient of variation (CV)6.886459
Kurtosis3386.3971
Mean18.663058
Median Absolute Deviation (MAD)3
Skewness49.580056
Sum227540
Variance16518.003
MonotonicityNot monotonic
2026-01-14T11:03:01.153054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03335
27.4%
11497
12.3%
2980
 
8.0%
3726
 
6.0%
4559
 
4.6%
5429
 
3.5%
6349
 
2.9%
7300
 
2.5%
8286
 
2.3%
9248
 
2.0%
Other values (304)3483
28.6%
ValueCountFrequency (%)
03335
27.4%
11497
12.3%
2980
 
8.0%
3726
 
6.0%
4559
 
4.6%
5429
 
3.5%
6349
 
2.9%
7300
 
2.5%
8286
 
2.3%
9248
 
2.0%
ValueCountFrequency (%)
100431
< 0.1%
55091
< 0.1%
31611
< 0.1%
31561
< 0.1%
21231
< 0.1%
19081
< 0.1%
17031
< 0.1%
14381
< 0.1%
13171
< 0.1%
13061
< 0.1%

DOI
Text

Missing 

Distinct10106
Distinct (%)99.8%
Missing2068
Missing (%)17.0%
Memory size801.5 KiB
2026-01-14T11:03:01.432104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length66
Median length58
Mean length25.522916
Min length12

Characters and Unicode

Total characters258394
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10088 ?
Unique (%)99.6%

Sample

1st row10.1016/j.tsc.2025.102068
2nd row10.1016/j.tsc.2025.102070
3rd row10.1016/j.tsc.2025.102056
4th row10.1016/j.tsc.2025.102049
5th row10.1016/j.neunet.2025.108407
ValueCountFrequency (%)
10.1007/s11423-023-10328-82
 
< 0.1%
10.1051/e3sconf/2024538050342
 
< 0.1%
10.1145/1140124.11401612
 
< 0.1%
10.1016/j.procir.2024.10.1612
 
< 0.1%
10.1145/3159450.31595862
 
< 0.1%
10.1016/b978-0-12-809324-5.23765-62
 
< 0.1%
10.1016/b978-044451719-7/50072-x2
 
< 0.1%
10.1016/b978-0-12-804071-3.00012-42
 
< 0.1%
10.4324/97813512323572
 
< 0.1%
10.34190/gbl.20.1562
 
< 0.1%
Other values (10096)10104
99.8%
2026-01-14T11:03:01.860178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
141207
15.9%
040154
15.5%
.22455
 
8.7%
218979
 
7.3%
315621
 
6.0%
913345
 
5.2%
712030
 
4.7%
411597
 
4.5%
511450
 
4.4%
811024
 
4.3%
Other values (64)60532
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)258394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
141207
15.9%
040154
15.5%
.22455
 
8.7%
218979
 
7.3%
315621
 
6.0%
913345
 
5.2%
712030
 
4.7%
411597
 
4.5%
511450
 
4.4%
811024
 
4.3%
Other values (64)60532
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)258394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
141207
15.9%
040154
15.5%
.22455
 
8.7%
218979
 
7.3%
315621
 
6.0%
913345
 
5.2%
712030
 
4.7%
411597
 
4.5%
511450
 
4.4%
811024
 
4.3%
Other values (64)60532
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)258394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
141207
15.9%
040154
15.5%
.22455
 
8.7%
218979
 
7.3%
315621
 
6.0%
913345
 
5.2%
712030
 
4.7%
411597
 
4.5%
511450
 
4.4%
811024
 
4.3%
Other values (64)60532
23.4%

Link
URL

Unique 

Distinct12192
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956727537&doi=10.1016%2FS0166-4115%2808%2962638-2&partnerID=40&md5=fd488215e393375baaef12aa42725781
 
1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0021727627&partnerID=40&md5=be2dc96ebd9234e597d319116bb12972
 
1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022320446&partnerID=40&md5=aeb5b56ef783c91f87cacc651e50b261
 
1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990557138&doi=10.1111%2Fj.1467-8640.1986.tb00069.x&partnerID=40&md5=b2a6bd5a8668ee647e8ce864f55cb012
 
1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168894514&partnerID=40&md5=5763976e0aa6d840c46ea85e4cb077a3
 
1
Other values (12187)
12187 
ValueCountFrequency (%)
https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956727537&doi=10.1016%2FS0166-4115%2808%2962638-2&partnerID=40&md5=fd488215e393375baaef12aa427257811
 
< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0021727627&partnerID=40&md5=be2dc96ebd9234e597d319116bb129721
 
< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022320446&partnerID=40&md5=aeb5b56ef783c91f87cacc651e50b2611
 
< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990557138&doi=10.1111%2Fj.1467-8640.1986.tb00069.x&partnerID=40&md5=b2a6bd5a8668ee647e8ce864f55cb0121
 
< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168894514&partnerID=40&md5=5763976e0aa6d840c46ea85e4cb077a31
 
< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0012101234&doi=10.1007%2FBF00051821&partnerID=40&md5=905960b3eac253fbdd5fc77151eccbe21
 
< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0012587120&doi=10.1145%2F6592.214913&partnerID=40&md5=a02ed9e48a4616e58d88191418edb7781
 
< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022959691&partnerID=40&md5=491fb3b0be55e6221fbd1520bc2905951
 
< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928454782&doi=10.1080%2F00201748708602112&partnerID=40&md5=c9a3342d0d441146ba199cab5ebcdb261
 
< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950876142&doi=10.1080%2F02693798708927821&partnerID=40&md5=2ea5dc98ab5770c4303d68a60e662d8f1
 
< 0.1%
Other values (12182)12182
99.9%
ValueCountFrequency (%)
https12192
100.0%
ValueCountFrequency (%)
www.scopus.com12192
100.0%
ValueCountFrequency (%)
/inward/record.uri12192
100.0%
ValueCountFrequency (%)
eid=2-s2.0-77956727537&doi=10.1016%2FS0166-4115%2808%2962638-2&partnerID=40&md5=fd488215e393375baaef12aa427257811
 
< 0.1%
eid=2-s2.0-0021727627&partnerID=40&md5=be2dc96ebd9234e597d319116bb129721
 
< 0.1%
eid=2-s2.0-0022320446&partnerID=40&md5=aeb5b56ef783c91f87cacc651e50b2611
 
< 0.1%
eid=2-s2.0-84990557138&doi=10.1111%2Fj.1467-8640.1986.tb00069.x&partnerID=40&md5=b2a6bd5a8668ee647e8ce864f55cb0121
 
< 0.1%
eid=2-s2.0-85168894514&partnerID=40&md5=5763976e0aa6d840c46ea85e4cb077a31
 
< 0.1%
eid=2-s2.0-0012101234&doi=10.1007%2FBF00051821&partnerID=40&md5=905960b3eac253fbdd5fc77151eccbe21
 
< 0.1%
eid=2-s2.0-0012587120&doi=10.1145%2F6592.214913&partnerID=40&md5=a02ed9e48a4616e58d88191418edb7781
 
< 0.1%
eid=2-s2.0-0022959691&partnerID=40&md5=491fb3b0be55e6221fbd1520bc2905951
 
< 0.1%
eid=2-s2.0-84928454782&doi=10.1080%2F00201748708602112&partnerID=40&md5=c9a3342d0d441146ba199cab5ebcdb261
 
< 0.1%
eid=2-s2.0-84950876142&doi=10.1080%2F02693798708927821&partnerID=40&md5=2ea5dc98ab5770c4303d68a60e662d8f1
 
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Other values (12182)12182
99.9%
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12192
100.0%

Affiliations
Text

Missing 

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Distinct (%)80.2%
Missing558
Missing (%)4.6%
Memory size2.8 MiB
2026-01-14T11:03:02.205270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2979
Median length628
Mean length149.5618
Min length6

Characters and Unicode

Total characters1740002
Distinct characters175
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8288 ?
Unique (%)71.2%

Sample

1st rowNanjing Normal University, Nanjing, Jiangsu, China
2nd rowFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China
3rd rowDepartment of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan
4th rowGeneral Directorate of Innovation and Educational Technologies, Ankara, Turkey; Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey
5th rowRadboud Universiteit, Nijmegen, Gelderland, Netherlands; Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain
ValueCountFrequency (%)
of16447
 
7.5%
university11348
 
5.2%
united7838
 
3.6%
states6817
 
3.1%
department5587
 
2.6%
and4993
 
2.3%
de2902
 
1.3%
education2782
 
1.3%
china2570
 
1.2%
science2559
 
1.2%
Other values (10094)155239
70.9%
2026-01-14T11:03:02.700728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
207445
 
11.9%
e139230
 
8.0%
n127950
 
7.4%
i124838
 
7.2%
a123277
 
7.1%
t106130
 
6.1%
o91692
 
5.3%
,77281
 
4.4%
r76086
 
4.4%
s60614
 
3.5%
Other values (165)605459
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1740002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
207445
 
11.9%
e139230
 
8.0%
n127950
 
7.4%
i124838
 
7.2%
a123277
 
7.1%
t106130
 
6.1%
o91692
 
5.3%
,77281
 
4.4%
r76086
 
4.4%
s60614
 
3.5%
Other values (165)605459
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1740002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
207445
 
11.9%
e139230
 
8.0%
n127950
 
7.4%
i124838
 
7.2%
a123277
 
7.1%
t106130
 
6.1%
o91692
 
5.3%
,77281
 
4.4%
r76086
 
4.4%
s60614
 
3.5%
Other values (165)605459
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1740002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
207445
 
11.9%
e139230
 
8.0%
n127950
 
7.4%
i124838
 
7.2%
a123277
 
7.1%
t106130
 
6.1%
o91692
 
5.3%
,77281
 
4.4%
r76086
 
4.4%
s60614
 
3.5%
Other values (165)605459
34.8%
Distinct11330
Distinct (%)96.0%
Missing384
Missing (%)3.1%
Memory size5.7 MiB
2026-01-14T11:03:03.095586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4955
Median length943.5
Mean length317.91421
Min length10

Characters and Unicode

Total characters3753931
Distinct characters219
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10960 ?
Unique (%)92.8%

Sample

1st rowWang, Yang, Nanjing Normal University, Nanjing, Jiangsu, China
2nd rowLin, Yuru, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Zhang, Yi, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Yang, Yuqin, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Pan, Shidan, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Ren, Xu, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Chen, Dengkang, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China
3rd rowHsu, Tingchia, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan; Hsu, Taiping, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan
4th rowAksoy, Behiye Dinçer, General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Mumcu, Filiz Kuşkaya, Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Cantürk Günhan, Berna, Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey
5th rowvan Bergen, Ruben S., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Huebotter, Justus F., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; null, null, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain; Lanillos, Pablo, Radboud Universiteit, Nijmegen, Gelderland, Netherlands, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain
ValueCountFrequency (%)
of28388
 
6.0%
university19900
 
4.2%
united13487
 
2.8%
states11963
 
2.5%
department9040
 
1.9%
and8643
 
1.8%
china5560
 
1.2%
de5551
 
1.2%
education4712
 
1.0%
science4637
 
1.0%
Other values (36362)363675
76.5%
2026-01-14T11:03:03.679631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
463999
 
12.4%
e283764
 
7.6%
a279890
 
7.5%
n268021
 
7.1%
i263180
 
7.0%
,214281
 
5.7%
t198701
 
5.3%
o188030
 
5.0%
r162102
 
4.3%
s123421
 
3.3%
Other values (209)1308542
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)3753931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
463999
 
12.4%
e283764
 
7.6%
a279890
 
7.5%
n268021
 
7.1%
i263180
 
7.0%
,214281
 
5.7%
t198701
 
5.3%
o188030
 
5.0%
r162102
 
4.3%
s123421
 
3.3%
Other values (209)1308542
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3753931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
463999
 
12.4%
e283764
 
7.6%
a279890
 
7.5%
n268021
 
7.1%
i263180
 
7.0%
,214281
 
5.7%
t198701
 
5.3%
o188030
 
5.0%
r162102
 
4.3%
s123421
 
3.3%
Other values (209)1308542
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3753931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
463999
 
12.4%
e283764
 
7.6%
a279890
 
7.5%
n268021
 
7.1%
i263180
 
7.0%
,214281
 
5.7%
t198701
 
5.3%
o188030
 
5.0%
r162102
 
4.3%
s123421
 
3.3%
Other values (209)1308542
34.9%
Distinct11972
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size35.8 MiB
2026-01-14T11:03:04.333653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13301
Median length2186.5
Mean length1309.4096
Min length23

Characters and Unicode

Total characters15964322
Distinct characters432
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11934 ?
Unique (%)97.9%

Sample

1st rowLearning engagement is an important indicator of active learning outcomes. Computational thinking is a basic competency required in the 21st century. Troubleshooting learning is helpful to enhance students’ computational thinking and engagement, as its targeted error analysis addresses traditional learning’s limitation of insufficient guidance on error-prone points. However, the role of troubleshooting in students’ engagement and computational thinking in robotics programming learning is to be explored. To fill in this gap, the current study explored the effects of troubleshooting robotics programming learning on students’ engagement, computational thinking, and programming skills. A quasi-experimental study was conducted to explore the effects of troubleshooting learning on students’ robotics programming learning by comparing students’ learning results in two courses instructed by the same instructor (one instructed with a problem-based method, the other instructed with a troubleshooting method). The participants were seventy-nine students from a university in China. Questionnaires, tests, and work analyses were used to measure students’ engagement, computational thinking, and programming skills. The results indicated that troubleshooting learning is more effective in enhancing students’ engagement (i.e., behavioral, cognitive, and emotional engagement), computational thinking (i.e., cooperativity, critical thinking, and creativity) and programming learning (i.e., data representation). The findings provide insight into troubleshooting-supported robotics programming learning. Different types of troubleshooting tasks with progressive difficulty are effective in enhancing students’ learning. Troubleshooting could be used in the early stages of programming learning to help students master the error prone areas of programming. © 2025 Elsevier Ltd.
2nd rowThe integration of artificial intelligence (AI) tools in education to promote computational thinking (CT) among students has become a trending topic of research; however, there is no consensus on the impact of such tools on CT. Qualitative syntheses regarding both the effect of AI tools and how to unleash their power more effectively are also lacking. Using a three-level meta-analytic approach, this study evaluated the effectiveness of AI tools in improving students’ CT and investigated the various moderating variables. A total of 32 empirical studies with 44 effect sizes were included in this meta-analysis, and the results showed that AI tools have a significant and moderately large effect on students’ CT (Hedges’s g = 0.75, 95 % CI [0.55, 0.95], p < 0.0001). Moderator analyses revealed that AI technologies, the application of AI tools, as well as tool customization and its method, and sample size significantly influence the effectiveness of AI tools. Other moderators—including region, publication year, subject disciplines, instructional approach, collaboration type, intervention duration, gender, and educational level—appeared to be universally effective in promoting student CT. Overall, this meta-analysis contributes to both the academic understanding and practical application of AI tools in CT education to help students prepare for the smart society of the future. © 2025 Elsevier Ltd.
3rd rowThe study developed an online game system for young students to learn computational thinking (CT), and explored the CT learning achievements and self-efficacy of students using two thinking-guided methods. One method was 5W1H, which is well known in science learning, and the other was concept-association-based concept mapping (CABCM). These thinking-guided methods, aimed at the beginning stage of problem analysis, were utilized before playing the online game, with the aim of helping students learn and solve CT tasks in the game scenarios. The research involved 54 students whose average age was 10, divided into two groups based on the different thinking-guided methods. The experimental results showed that students in both the CABCM and 5W1H groups demonstrated significant learning gains in CT achievement and self-efficacy from pre-test to post-test. While no statistically significant difference was found in the post-test scores between the two groups, a detailed analysis of learning behaviors revealed distinct problem-solving pathways associated with each thinking-guided method. The findings suggest that both integrated approaches effectively fostered CT skills, albeit through different cognitive processes. This research contributes to CT education by integrating thinking-guided methods into an online CT game. It offers empirical evidence on the effectiveness of such integrated approaches and provides insights into the processes and behaviors associated with different thinking-guided methods, shedding light on students' challenges in learning CT through games. © © 2025. Published by Elsevier Ltd.
4th rowThis study explores how Computational Thinking (CT) components overlap with the phases of mathematical modelling within the context of a Teacher Development Course (TDC). The course was designed, developed, implemented, and assessed to enhance teachers’ cognitive actions in integrating CT with mathematical modelling. This research study was conducted with three mathematics teachers and one computer science teacher. Data were collected through CT component worksheets and video recordings, and analysed based on Borromeo-Ferri’s (2006) modelling cycle and the study’s CT framework. The study’s findings indicate that modelling processes enhanced teachers’ CT skills, while CT components made the modelling process more structured and reflective, revealing a reciprocal relationship between modelling and CT. The study proposes an original interdisciplinary framework linking teachers’ cognitive actions to CT integration, offering both theoretical and practical contributions. © 2025 The Author(s).
5th rowAutonomous intelligent agents must bridge computational challenges at disparate levels of abstraction, from the low-level spaces of sensory input and motor commands to the high-level domain of abstract reasoning and planning. A key question in designing such agents is how best to instantiate the representational space that will interface between these two levels—ideally without requiring supervision in the form of expensive data annotations. These objectives can be efficiently achieved by representing the world in terms of objects (grounded in perception and action). In this work, we present a novel, brain-inspired, deep-learning architecture that learns from pixels to interpret, control, and reason about its environment, using object-centric representations. We show the utility of our approach through tasks in synthetic environments that require a combination of (high-level) logical reasoning and (low-level) continuous control. Results show that the agent can learn emergent conditional behavioural reasoning, such as (A → B)∧(¬A → C), as well as logical composition (A → B)∧(A → C)⊢A → (B∧C) and XOR operations, and successfully controls its environment to satisfy objectives deduced from these logical rules. The agent can adapt online to unexpected changes in its environment and is robust to mild violations of its world model, thanks to dynamic internal desired goal generation. While the present results are limited to synthetic settings (2D and 3D activated versions of dSprites), which fall short of real-world levels of complexity, the proposed architecture shows how to manipulate grounded object representations, as a key inductive bias for unsupervised learning, to enable behavioral reasoning. © 2025 The Author(s)
ValueCountFrequency (%)
the128221
 
5.6%
and88557
 
3.9%
of87066
 
3.8%
to58800
 
2.6%
in57532
 
2.5%
a43907
 
1.9%
for25277
 
1.1%
that22114
 
1.0%
is21623
 
0.9%
this21381
 
0.9%
Other values (57066)1729718
75.7%
2026-01-14T11:03:05.332497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2270696
14.2%
e1519726
 
9.5%
t1164801
 
7.3%
i1105917
 
6.9%
n1045031
 
6.5%
a1013964
 
6.4%
o947035
 
5.9%
s872806
 
5.5%
r778827
 
4.9%
c551279
 
3.5%
Other values (422)4694240
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)15964322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2270696
14.2%
e1519726
 
9.5%
t1164801
 
7.3%
i1105917
 
6.9%
n1045031
 
6.5%
a1013964
 
6.4%
o947035
 
5.9%
s872806
 
5.5%
r778827
 
4.9%
c551279
 
3.5%
Other values (422)4694240
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15964322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2270696
14.2%
e1519726
 
9.5%
t1164801
 
7.3%
i1105917
 
6.9%
n1045031
 
6.5%
a1013964
 
6.4%
o947035
 
5.9%
s872806
 
5.5%
r778827
 
4.9%
c551279
 
3.5%
Other values (422)4694240
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15964322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2270696
14.2%
e1519726
 
9.5%
t1164801
 
7.3%
i1105917
 
6.9%
n1045031
 
6.5%
a1013964
 
6.4%
o947035
 
5.9%
s872806
 
5.5%
r778827
 
4.9%
c551279
 
3.5%
Other values (422)4694240
29.4%

Author Keywords
Text

Missing 

Distinct9188
Distinct (%)99.1%
Missing2918
Missing (%)23.9%
Memory size1.4 MiB
2026-01-14T11:03:05.817419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length930
Median length278
Mean length97.580116
Min length6

Characters and Unicode

Total characters904958
Distinct characters141
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9106 ?
Unique (%)98.2%

Sample

1st rowComputational thinking; Programming skills; Robotics programming learning; Troubleshooting
2nd rowArtificial intelligence; Artificial intelligence in education; Computational thinking; Moderator analysis; Three-level meta-analysis
3rd row5W1H; Computational thinking; Concept-association-based concept mapping strategy; Self-efficacy
4th rowComputational thinking; CT components; CT-integrated maths education; Mathematical modelling; Teacher development
5th rowBrain-inspired perception and control; Deep learning architectures; Object-centric reasoning
ValueCountFrequency (%)
computational5920
 
6.4%
thinking5876
 
6.3%
education3133
 
3.4%
learning2605
 
2.8%
programming1708
 
1.8%
science1181
 
1.3%
design1127
 
1.2%
computer939
 
1.0%
and795
 
0.9%
of684
 
0.7%
Other values (9696)69032
74.2%
2026-01-14T11:03:06.524784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83716
 
9.3%
i79123
 
8.7%
n70955
 
7.8%
e66177
 
7.3%
t64142
 
7.1%
a62228
 
6.9%
o56863
 
6.3%
r39633
 
4.4%
;37871
 
4.2%
l36114
 
4.0%
Other values (131)308136
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)904958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
83716
 
9.3%
i79123
 
8.7%
n70955
 
7.8%
e66177
 
7.3%
t64142
 
7.1%
a62228
 
6.9%
o56863
 
6.3%
r39633
 
4.4%
;37871
 
4.2%
l36114
 
4.0%
Other values (131)308136
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)904958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
83716
 
9.3%
i79123
 
8.7%
n70955
 
7.8%
e66177
 
7.3%
t64142
 
7.1%
a62228
 
6.9%
o56863
 
6.3%
r39633
 
4.4%
;37871
 
4.2%
l36114
 
4.0%
Other values (131)308136
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)904958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
83716
 
9.3%
i79123
 
8.7%
n70955
 
7.8%
e66177
 
7.3%
t64142
 
7.1%
a62228
 
6.9%
o56863
 
6.3%
r39633
 
4.4%
;37871
 
4.2%
l36114
 
4.0%
Other values (131)308136
34.0%

Index Keywords
Text

Missing 

Distinct7351
Distinct (%)99.8%
Missing4826
Missing (%)39.6%
Memory size2.5 MiB
2026-01-14T11:03:06.992939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2655
Median length692
Mean length275.7107
Min length7

Characters and Unicode

Total characters2030885
Distinct characters119
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7336 ?
Unique (%)99.6%

Sample

1st rowAbstracting; Architecture; Behavioral research; Deep learning; Intelligent agents; Memory architecture; Unsupervised learning; Autonomous Intelligent Agents; Behavioral reasoning; Brain-inspired; Brain-inspired perception and control; Computational challenges; Deep learning architecture; Learn+; Learning architectures; Object-centric reasoning; Sensory motors; Autonomous agents; abstract thinking; article; clinical article; controlled study; deep learning; human; learning; logical reasoning; reasoning; sensory stimulation
2nd rowComputational methods; E-learning; Teaching; Academic achievements; Computational thinkings; Computer literacy; Digital competency; Digital skills; ICT use; Information literacy; Mediation effect; Multi-group; Self efficacy; Students
3rd rowBrain; Cognitive systems; Dynamics; Neural networks; Neurons; Stability; Meta-stable state; Multiple neural timescale; Neural activity; Sequential patterns; Speed modulation; Task difficulty; Temporal modulations; Temporal scaling; Time-scales; Working memory; Computation theory; article; artificial neural network; cognition; controlled study; dwell time; human experiment; learning; mental performance; nerve cell network; nonhuman; speech; thinking; velocity; working memory
4th rowComputational methods; Machine learning; Current modeling; Explainability; Learn+; Phase 1; Pre-training; Radiology report generation; Radiology reports; Reinforcement learnings; Report generation; Training framework; Radiology; article; benchmarking; human; large language model; radiologist; reasoning; thinking; thorax radiography; X ray; X ray analysis
5th rowArtificial intelligence; Fuel additives; Input output programs; Iterative methods; Learning systems; Systems analysis; Systems thinking; Test facilities; Active Learning; Adaptive sampling; Cluster-based; Computational effort; Machine-learning; Real-world; Sampling technique; Surrogate modeling; System models; Test-functions; Design of experiments
ValueCountFrequency (%)
computational6807
 
3.3%
education4929
 
2.4%
learning4707
 
2.3%
computer3876
 
1.9%
thinkings3669
 
1.8%
programming3221
 
1.5%
students3139
 
1.5%
systems2864
 
1.4%
engineering2514
 
1.2%
thinking2194
 
1.1%
Other values (11786)170780
81.8%
2026-01-14T11:03:07.755499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
201334
 
9.9%
e162044
 
8.0%
i158684
 
7.8%
n150294
 
7.4%
t136501
 
6.7%
a131589
 
6.5%
o124227
 
6.1%
;102182
 
5.0%
s99565
 
4.9%
r98152
 
4.8%
Other values (109)666313
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)2030885
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
201334
 
9.9%
e162044
 
8.0%
i158684
 
7.8%
n150294
 
7.4%
t136501
 
6.7%
a131589
 
6.5%
o124227
 
6.1%
;102182
 
5.0%
s99565
 
4.9%
r98152
 
4.8%
Other values (109)666313
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2030885
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
201334
 
9.9%
e162044
 
8.0%
i158684
 
7.8%
n150294
 
7.4%
t136501
 
6.7%
a131589
 
6.5%
o124227
 
6.1%
;102182
 
5.0%
s99565
 
4.9%
r98152
 
4.8%
Other values (109)666313
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2030885
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
201334
 
9.9%
e162044
 
8.0%
i158684
 
7.8%
n150294
 
7.4%
t136501
 
6.7%
a131589
 
6.5%
o124227
 
6.1%
;102182
 
5.0%
s99565
 
4.9%
r98152
 
4.8%
Other values (109)666313
32.8%
Distinct4
Distinct (%)100.0%
Missing12188
Missing (%)> 99.9%
Memory size381.6 KiB
2026-01-14T11:03:08.079718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length223
Median length98
Mean length116
Min length45

Characters and Unicode

Total characters464
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowGENBANK: KP311695:KP311894, KP311895:KP311944
2nd rowSWISSPROT: P29466, P31944, P42575, P49662, P51878, P55210, P55211, P55212, Q14790, Q92851
3rd rowPIR: O60675, P05164, P13693, P17480, P30626, P31948, P33241, P35398, P51858, Q13951, Q14103, Q14393, Q92945
4th rowGENBANK: RS105147, RS165599, RS165656, RS165688, RS174682, RS174694, RS284786, RS284787, RS475325, RS581105, RS598156, RS6263, RS6323, RS729147, RS740603, RS887241, RS894369, RS917520, RS921450, RS933269, RS938328, RS971074
ValueCountFrequency (%)
genbank2
 
3.9%
kp311695:kp3118941
 
2.0%
kp311895:kp3119441
 
2.0%
swissprot1
 
2.0%
p294661
 
2.0%
p319441
 
2.0%
p425751
 
2.0%
p496621
 
2.0%
p518781
 
2.0%
p552101
 
2.0%
Other values (40)40
78.4%
2026-01-14T11:03:08.462094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
 
10.1%
,43
 
9.3%
142
 
9.1%
533
 
7.1%
429
 
6.2%
929
 
6.2%
627
 
5.8%
327
 
5.8%
S25
 
5.4%
R24
 
5.2%
Other values (17)138
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
47
 
10.1%
,43
 
9.3%
142
 
9.1%
533
 
7.1%
429
 
6.2%
929
 
6.2%
627
 
5.8%
327
 
5.8%
S25
 
5.4%
R24
 
5.2%
Other values (17)138
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
47
 
10.1%
,43
 
9.3%
142
 
9.1%
533
 
7.1%
429
 
6.2%
929
 
6.2%
627
 
5.8%
327
 
5.8%
S25
 
5.4%
R24
 
5.2%
Other values (17)138
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
47
 
10.1%
,43
 
9.3%
142
 
9.1%
533
 
7.1%
429
 
6.2%
929
 
6.2%
627
 
5.8%
327
 
5.8%
S25
 
5.4%
R24
 
5.2%
Other values (17)138
29.7%

Chemicals/CAS
Text

Missing 

Distinct123
Distinct (%)92.5%
Missing12059
Missing (%)98.9%
Memory size395.8 KiB
2026-01-14T11:03:08.744637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length872
Median length147
Mean length95.842105
Min length5

Characters and Unicode

Total characters12747
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)88.0%

Sample

1st rowalanine aminotransferase, 9000-86-6, 9014-30-6
2nd rowketamine, 1867-66-9, 6740-88-1, 81771-21-3; temozolomide, 85622-93-1; transforming growth factor beta receptor 1; transforming growth factor beta receptor 2; xylazine, 23076-35-9, 7361-61-7; Smad protein, 62395-38-4; MicroRNAs; MIRN590 microRNA, human; Octamer Transcription Factor-3; poly(beta-amino ester); Polymers; POU5F1 protein, human; Receptor, Transforming Growth Factor-beta Type II; Smad Proteins; SOX2 protein, human; SOXB1 Transcription Factors; Temozolomide; TGFBR2 protein, human
3rd rowamino acid, 65072-01-7; Chromatin
4th rowcarbon dioxide, 124-38-9, 58561-67-4
5th rowcarbon, 7440-44-0; nitrophenol, 25154-55-6; Carbon; Nitrophenols; Water Pollutants, Chemical
ValueCountFrequency (%)
protein47
 
3.5%
038
 
2.8%
proteins25
 
1.8%
rna24
 
1.8%
dna21
 
1.5%
dopamine17
 
1.3%
human16
 
1.2%
acid15
 
1.1%
51-61-614
 
1.0%
kinase13
 
1.0%
Other values (762)1126
83.0%
2026-01-14T11:03:09.188724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1223
 
9.6%
-785
 
6.2%
e728
 
5.7%
i612
 
4.8%
n560
 
4.4%
a557
 
4.4%
o549
 
4.3%
r475
 
3.7%
,461
 
3.6%
t461
 
3.6%
Other values (58)6336
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)12747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1223
 
9.6%
-785
 
6.2%
e728
 
5.7%
i612
 
4.8%
n560
 
4.4%
a557
 
4.4%
o549
 
4.3%
r475
 
3.7%
,461
 
3.6%
t461
 
3.6%
Other values (58)6336
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1223
 
9.6%
-785
 
6.2%
e728
 
5.7%
i612
 
4.8%
n560
 
4.4%
a557
 
4.4%
o549
 
4.3%
r475
 
3.7%
,461
 
3.6%
t461
 
3.6%
Other values (58)6336
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1223
 
9.6%
-785
 
6.2%
e728
 
5.7%
i612
 
4.8%
n560
 
4.4%
a557
 
4.4%
o549
 
4.3%
r475
 
3.7%
,461
 
3.6%
t461
 
3.6%
Other values (58)6336
49.7%

Tradenames
Text

Missing 

Distinct18
Distinct (%)100.0%
Missing12174
Missing (%)99.9%
Memory size382.1 KiB
2026-01-14T11:03:09.455518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length179
Median length26.5
Mean length35.722222
Min length5

Characters and Unicode

Total characters643
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)100.0%

Sample

1st rowAttune NxT flow cytometer, Thermo; MAGnify Chromatin Immunoprecipitation System, Life Technologies; Odyssey Infrared Imager, LI COR; SCpubr R package v1.1.2; Seurat R package v4.4
2nd rowMAXQDA software Version 2022; Smart-PLS software Version 4.0
3rd rowMATLAB; Psychtoolbox-3; rstan package in R; Siemens Prisma MRI scanner, Siemens
4th rowCytoscape
5th rowHydroCel, Electrical Geodesics
ValueCountFrequency (%)
siemens8
 
9.1%
r3
 
3.4%
package3
 
3.4%
system2
 
2.3%
software2
 
2.3%
tim2
 
2.3%
trio2
 
2.3%
alere2
 
2.3%
scanner2
 
2.3%
version2
 
2.3%
Other values (59)60
68.2%
2026-01-14T11:03:09.847217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
 
10.9%
e62
 
9.6%
i35
 
5.4%
a35
 
5.4%
n34
 
5.3%
r32
 
5.0%
s31
 
4.8%
o28
 
4.4%
m23
 
3.6%
t23
 
3.6%
Other values (46)270
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
70
 
10.9%
e62
 
9.6%
i35
 
5.4%
a35
 
5.4%
n34
 
5.3%
r32
 
5.0%
s31
 
4.8%
o28
 
4.4%
m23
 
3.6%
t23
 
3.6%
Other values (46)270
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
70
 
10.9%
e62
 
9.6%
i35
 
5.4%
a35
 
5.4%
n34
 
5.3%
r32
 
5.0%
s31
 
4.8%
o28
 
4.4%
m23
 
3.6%
t23
 
3.6%
Other values (46)270
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
70
 
10.9%
e62
 
9.6%
i35
 
5.4%
a35
 
5.4%
n34
 
5.3%
r32
 
5.0%
s31
 
4.8%
o28
 
4.4%
m23
 
3.6%
t23
 
3.6%
Other values (46)270
42.0%

Manufacturers
Text

Missing 

Distinct6
Distinct (%)66.7%
Missing12183
Missing (%)99.9%
Memory size381.4 KiB
2026-01-14T11:03:09.988663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length20
Mean length11.777778
Min length5

Characters and Unicode

Total characters106
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)55.6%

Sample

1st rowThermo; Life Technologies; LI COR
2nd rowSiemens
3rd rowElectrical Geodesics
4th rowApple
5th rowAlere; Siemens
ValueCountFrequency (%)
siemens5
33.3%
thermo1
 
6.7%
life1
 
6.7%
technologies1
 
6.7%
li1
 
6.7%
cor1
 
6.7%
electrical1
 
6.7%
geodesics1
 
6.7%
apple1
 
6.7%
alere1
 
6.7%
2026-01-14T11:03:10.230762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e20
18.9%
i10
 
9.4%
s8
 
7.5%
n7
 
6.6%
m6
 
5.7%
6
 
5.7%
S5
 
4.7%
l5
 
4.7%
o4
 
3.8%
c4
 
3.8%
Other values (19)31
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e20
18.9%
i10
 
9.4%
s8
 
7.5%
n7
 
6.6%
m6
 
5.7%
6
 
5.7%
S5
 
4.7%
l5
 
4.7%
o4
 
3.8%
c4
 
3.8%
Other values (19)31
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e20
18.9%
i10
 
9.4%
s8
 
7.5%
n7
 
6.6%
m6
 
5.7%
6
 
5.7%
S5
 
4.7%
l5
 
4.7%
o4
 
3.8%
c4
 
3.8%
Other values (19)31
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e20
18.9%
i10
 
9.4%
s8
 
7.5%
n7
 
6.6%
m6
 
5.7%
6
 
5.7%
S5
 
4.7%
l5
 
4.7%
o4
 
3.8%
c4
 
3.8%
Other values (19)31
29.2%

Funding Details
Text

Missing 

Distinct3412
Distinct (%)87.8%
Missing8305
Missing (%)68.1%
Memory size1019.0 KiB
2026-01-14T11:03:10.544785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3365
Median length435
Mean length110.01415
Min length5

Characters and Unicode

Total characters427625
Distinct characters172
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3144 ?
Unique (%)80.9%

Sample

1st rowMinistry of Education, MOE; Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province, (25JYC004)
2nd rowNational Natural Science Foundation of China, NSFC, (72274076); Fundamental Research Funds for the Central Universities, (30106250032)
3rd row(NSTC 111-2410-H-003-168-MY3)
4th rowNational Taiwan Normal University, NTNU; International Association for the Evaluation of Educational Achievement, IEA; National Science and Technology Council, NSTC; Ministry of Education, MOE
5th rowEconomic and Social Research Council, SSRC, (2267832)
ValueCountFrequency (%)
science2802
 
5.5%
of2772
 
5.4%
national2683
 
5.3%
foundation2435
 
4.8%
nsf1486
 
2.9%
and1232
 
2.4%
research1062
 
2.1%
de869
 
1.7%
university758
 
1.5%
china745
 
1.5%
Other values (7838)34045
66.9%
2026-01-14T11:03:11.031294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47001
 
11.0%
n27796
 
6.5%
a24581
 
5.7%
i24013
 
5.6%
e23758
 
5.6%
o22604
 
5.3%
t15582
 
3.6%
c13351
 
3.1%
,12837
 
3.0%
011568
 
2.7%
Other values (162)204534
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)427625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
47001
 
11.0%
n27796
 
6.5%
a24581
 
5.7%
i24013
 
5.6%
e23758
 
5.6%
o22604
 
5.3%
t15582
 
3.6%
c13351
 
3.1%
,12837
 
3.0%
011568
 
2.7%
Other values (162)204534
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)427625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
47001
 
11.0%
n27796
 
6.5%
a24581
 
5.7%
i24013
 
5.6%
e23758
 
5.6%
o22604
 
5.3%
t15582
 
3.6%
c13351
 
3.1%
,12837
 
3.0%
011568
 
2.7%
Other values (162)204534
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)427625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
47001
 
11.0%
n27796
 
6.5%
a24581
 
5.7%
i24013
 
5.6%
e23758
 
5.6%
o22604
 
5.3%
t15582
 
3.6%
c13351
 
3.1%
,12837
 
3.0%
011568
 
2.7%
Other values (162)204534
47.8%

Funding Texts
Text

Missing 

Distinct4024
Distinct (%)97.0%
Missing8043
Missing (%)66.0%
Memory size3.4 MiB
2026-01-14T11:03:11.396149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length92320
Median length1268
Mean length458.8872
Min length4

Characters and Unicode

Total characters1903923
Distinct characters353
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3938 ?
Unique (%)94.9%

Sample

1st rowThis work was supported by the Project of Humanities and Social Sciences Program of the Ministry of Education , the Philosophy and Social Science Research project of Jiangsu province (No. 25JYC004 ).
2nd rowThis study was funded by the 2023 National Natural Science Foundation of China (Grant No. 72274076) and funded by the Fundamental Research Funds for the Central Universities (Outstanding Innovation Project, No. 30106250032).
3rd rowThis study is supported in part by the National Science and Technology Council in the Republic of China under contract numbers NSTC 111-2410-H-003-168-MY3 .
4th rowThe authors thank to the financial support from The National Science and Technology Council, Taiwan, and The Ministry of Education, Taiwan. Special thanks go to the colleagues from the IEA in Hamberg, Germany, the IEA in Amsterdam, Neitherlands, and the ICILS 2023 National Research Center at NTNU, Taiwan for their excellent research collaboration in the ICILS 2023 study.
5th rowFunding text 1: This research was funded by the Economic and Social Research Council, granted to Sarah A. Gerson and Johanna E. van Schaik, with in-kind contributions from Primo Toys/Moravia Education and Techniquest (reference 2267832).We are grateful to the schools, teachers, and children who participated in this research. We also thank the BSc and MSc students \u2013 Lloyd, Alexandra, Chara, Georgie, Zoe, Matt, Ellie, and Jamie \u2013 for their support with data processing. Special thanks to Dr Dominic Guitard and Dr Kelsey Frewin for their statistical advice, and to Vicky Simmons for her invaluable help in collecting the large volume of data. We would also like to extend our gratitude to the reviewers for providing fruitful feedback. We appreciate the support of Primo Toys/Moravia Consulting for providing essential resources. This project was funded by the Economic and Social Research Council through the Doctoral Training Partnership.; Funding text 2: This research was funded by the Economic and Social Research Council , granted to Sarah A. Gerson and Johanna E. van Schaik, with in-kind contributions from Primo Toys/Moravia Education and Techniquest (reference 2267832 ).
ValueCountFrequency (%)
the18688
 
6.8%
and11291
 
4.1%
of10245
 
3.7%
by5377
 
1.9%
this5231
 
1.9%
in4634
 
1.7%
for4530
 
1.6%
to4477
 
1.6%
research4450
 
1.6%
supported2943
 
1.1%
Other values (26623)204834
74.0%
2026-01-14T11:03:11.935568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
272527
 
14.3%
e152683
 
8.0%
n117118
 
6.2%
t114955
 
6.0%
a113835
 
6.0%
o108417
 
5.7%
i105775
 
5.6%
r95378
 
5.0%
s80596
 
4.2%
h58171
 
3.1%
Other values (343)684468
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1903923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
272527
 
14.3%
e152683
 
8.0%
n117118
 
6.2%
t114955
 
6.0%
a113835
 
6.0%
o108417
 
5.7%
i105775
 
5.6%
r95378
 
5.0%
s80596
 
4.2%
h58171
 
3.1%
Other values (343)684468
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1903923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
272527
 
14.3%
e152683
 
8.0%
n117118
 
6.2%
t114955
 
6.0%
a113835
 
6.0%
o108417
 
5.7%
i105775
 
5.6%
r95378
 
5.0%
s80596
 
4.2%
h58171
 
3.1%
Other values (343)684468
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1903923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
272527
 
14.3%
e152683
 
8.0%
n117118
 
6.2%
t114955
 
6.0%
a113835
 
6.0%
o108417
 
5.7%
i105775
 
5.6%
r95378
 
5.0%
s80596
 
4.2%
h58171
 
3.1%
Other values (343)684468
36.0%

References
Text

Missing 

Distinct11382
Distinct (%)99.4%
Missing740
Missing (%)6.1%
Memory size21.1 MiB
2026-01-14T11:03:12.404489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2254
Median length1422
Mean length1050.1524
Min length9

Characters and Unicode

Total characters12026345
Distinct characters265
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11317 ?
Unique (%)98.8%

Sample

1st rowAstin, Alexander W., Student involvement: A developmental theory for higher education, Journal of College Student Development, 40, 5, pp. 518-529, (1999); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); Bacca, Jorge, Student engagement with mobile-based assessment systems: A survival analysis, Journal of Computer Assisted Learning, 37, 1, pp. 158-171, (2021); Melander Bowden, Helen, Problem-solving in collaborative game design practices: epistemic stance, affect, and engagement, Learning, Media and Technology, 44, 2, pp. 124-143, (2019); APA Handbook of Research Methods in Psychology Research Designs Quantitative Qualitative Neuropsychological and Biological, (2023); Buil, Isabel, Engagement in business simulation games: A self-system model of motivational development, British Journal of Educational Technology, 51, 1, pp. 297-311, (2020); Çakır, Recep, The effect of robotic coding education on preschoolers’ problem solving and creative thinking skills, Thinking Skills and Creativity, 40, (2021); Thinking Skills and Creativity, (2021); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); undefined
2nd rowAldabe, Itziar, Semantic similarity measures for the generation of science tests in basque, IEEE Transactions on Learning Technologies, 7, 4, pp. 375-387, (2014); Ameen, Linda Talib, The Impact of Artificial Intelligence on Computational Thinking in Education at University, International Journal of Engineering Pedagogy, 14, 5, pp. 192-203, (2024); Angeli Valanides, Charoula Nicos, Investigating the effects of gender and scaffolding in developing preschool children’s computational thinking during problem-solving with Bee-Bots, Frontiers in Education, 7, (2023); Asunda, Paul A., Embracing Computational Thinking as an Impetus for Artificial Intelligence in Integrated STEM Disciplines through Engineering and Technology Education, Journal of Technology Education, 34, 2, pp. 43-63, (2023); Atkinson, Richard C., Human Memory: A Proposed System and its Control Processes, Psychology of Learning and Motivation - Advances in Research and Theory, 2, C, pp. 89-195, (1968); Jbi Manual for Evidence Synthesis, (2024); Educ AI Tion Rebooted Exploring the Future of Artificial Intelligence in Schools and Colleges, (2019); Basu, Satabdi, Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment, User Modeling and User-Adapted Interaction, 27, 1, pp. 5-53, (2017); Bhatt, Sohum Mandar, A Method for Developing Process-Based Assessments for Computational Thinking Tasks, Journal of Learning Analytics, 11, 2, pp. 157-173, (2024); Belland, Brian R., A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education, Review of Educational Research, 87, 6, pp. 1042-1081, (2017)
3rd rowAlsadoon, Elham, Effects of a gamified learning environment on students’ achievement, motivations, and satisfaction, Heliyon, 8, 8, (2022); Journal of Languages and Language Teaching, (2023); Avcı, Canan, Computational thinking: early childhood teachers’ and prospective teachers’ preconceptions and self-efficacy, Education and Information Technologies, 27, 8, pp. 11689-11713, (2022); Bakeman, Roger A., Observer agreement for timed-event sequential data: A comparison of time-based and event-based algorithms, Behavior Research Methods, 41, 1, pp. 137-147, (2009); Bers, Marina Umaschi, Computational thinking and tinkering: Exploration of an early childhood robotics curriculum, Computers and Education, 72, pp. 145-157, (2014); Annual American Educational Research Association Meeting, (2012); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); Cheng, Shuchen, Facilitating creativity, collaboration, and computational thinking in group website design: a concept mapping-based mobile flipped learning approach, International Journal of Mobile Learning and Organisation, 18, 2, pp. 169-193, (2024); Cheng, Yuping, Enhancing student's computational thinking skills with student-generated questions strategy in a game-based learning platform, Computers and Education, 200, (2023); Chevalier, Morgane, The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school, Computers and Education, 180, (2022)
4th rowTurkish Studies Educational Sciences, (2020); Mathematical Modelling Education in East and West, (2021); Journal of Theory and Practice in Education, (2017); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Mathematical Epistemology and Psychology, (1966); Journal of Mathematical Modelling and Application, (2009); Modelling and Applications in Mathematics Education, (2007); Mathematical Modelling Ictma 12 Education Engineering and Economics, (2007); Modelling Applications and Applied Problem Solving, (1989); Borromeo-Ferri, Rita, Theoretical and empirical differentiations of phases in the modelling process, ZDM - International Journal on Mathematics Education, 38, 2, pp. 86-95, (2006)
5th rowundefined, (2022); Iclr2022 Workshop on the Elements of Reasoning Objects Structure and Causality, (2022); undefined, (2025); Battaglia, Peter W., Interaction networks for learning about objects, relations and physics, Advances in Neural Information Processing Systems, pp. 4509-4517, (2016); Battaglia, Peter W., Simulation as an engine of physical scene understanding, Proceedings of the National Academy of Sciences of the United States of America, 110, 45, pp. 18327-18332, (2013); van Bergen, Ruben S., Object-Based Active Inference, Communications in Computer and Information Science, 1721 CCIS, pp. 50-64, (2023); Bas, Fred, Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind, Proceedings - IEEE International Conference on Robotics and Automation, 2022-January, pp. 5389-5395, (2022); Cowley, Stephen John, How human infants deal with symbol grounding, Interaction Studies, 8, 1, pp. 83-104, (2007); undefined, (2022); Driess, Danny, Learning Multi-Object Dynamics with Compositional Neural Radiance Fields, Proceedings of Machine Learning Research, 205, pp. 1755-1768, (2023)
ValueCountFrequency (%)
of63056
 
3.9%
and52726
 
3.3%
pp51469
 
3.2%
the37719
 
2.3%
in36346
 
2.2%
a24847
 
1.5%
education23077
 
1.4%
for18994
 
1.2%
thinking17393
 
1.1%
computational17173
 
1.1%
Other values (90024)1274852
78.8%
2026-01-14T11:03:13.045548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1606002
 
13.4%
e805206
 
6.7%
n785044
 
6.5%
i719409
 
6.0%
a673023
 
5.6%
o653918
 
5.4%
t575011
 
4.8%
r482124
 
4.0%
,429102
 
3.6%
s392720
 
3.3%
Other values (255)4904786
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)12026345
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1606002
 
13.4%
e805206
 
6.7%
n785044
 
6.5%
i719409
 
6.0%
a673023
 
5.6%
o653918
 
5.4%
t575011
 
4.8%
r482124
 
4.0%
,429102
 
3.6%
s392720
 
3.3%
Other values (255)4904786
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12026345
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1606002
 
13.4%
e805206
 
6.7%
n785044
 
6.5%
i719409
 
6.0%
a673023
 
5.6%
o653918
 
5.4%
t575011
 
4.8%
r482124
 
4.0%
,429102
 
3.6%
s392720
 
3.3%
Other values (255)4904786
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12026345
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1606002
 
13.4%
e805206
 
6.7%
n785044
 
6.5%
i719409
 
6.0%
a673023
 
5.6%
o653918
 
5.4%
t575011
 
4.8%
r482124
 
4.0%
,429102
 
3.6%
s392720
 
3.3%
Other values (255)4904786
40.8%
Distinct6581
Distinct (%)95.5%
Missing5302
Missing (%)43.5%
Memory size1.6 MiB
2026-01-14T11:03:13.431497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length899
Median length327
Mean length132.69434
Min length2

Characters and Unicode

Total characters914264
Distinct characters166
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6355 ?
Unique (%)92.2%

Sample

1st rowY. Wang; Adolescent Education and Intelligence Support Lab of Nanjing Normal University, Nanjing, China; email: wangyang@nnu.edu.cn
2nd rowY. Yang; Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, No. 152 Luoyu Road, Hubei, 430079, China; email: yangyuqin@ccnu.edu.cn
3rd rowT.-P. Hsu; Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei city, 162, Sec. 1, East Heping Rd, 10610, Taiwan; email: 81171002H@ntnu.edu.tw
4th rowF.K. Mumcu; Digitalization in Early Childhood Education, Department of Education Research and Teacher Education, University of Graz, Graz, Austria; email: filiz.mumcu@uni-graz.at
5th rowP. Lanillos; Donders Institute, Radboud University, Nijmegen, Netherlands; email: p.lanillos@csic.es
ValueCountFrequency (%)
of7282
 
6.8%
email7108
 
6.6%
university5229
 
4.9%
and2337
 
2.2%
department2190
 
2.0%
united1945
 
1.8%
states1638
 
1.5%
education1453
 
1.4%
science1146
 
1.1%
china1110
 
1.0%
Other values (19556)75991
70.7%
2026-01-14T11:03:14.010302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
100552
 
11.0%
e68670
 
7.5%
a66319
 
7.3%
i62711
 
6.9%
n57927
 
6.3%
t43799
 
4.8%
o43783
 
4.8%
r35041
 
3.8%
l31460
 
3.4%
s27553
 
3.0%
Other values (156)376449
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)914264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
100552
 
11.0%
e68670
 
7.5%
a66319
 
7.3%
i62711
 
6.9%
n57927
 
6.3%
t43799
 
4.8%
o43783
 
4.8%
r35041
 
3.8%
l31460
 
3.4%
s27553
 
3.0%
Other values (156)376449
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)914264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
100552
 
11.0%
e68670
 
7.5%
a66319
 
7.3%
i62711
 
6.9%
n57927
 
6.3%
t43799
 
4.8%
o43783
 
4.8%
r35041
 
3.8%
l31460
 
3.4%
s27553
 
3.0%
Other values (156)376449
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)914264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
100552
 
11.0%
e68670
 
7.5%
a66319
 
7.3%
i62711
 
6.9%
n57927
 
6.3%
t43799
 
4.8%
o43783
 
4.8%
r35041
 
3.8%
l31460
 
3.4%
s27553
 
3.0%
Other values (156)376449
41.2%

Editors
Text

Missing 

Distinct1266
Distinct (%)45.9%
Missing9435
Missing (%)77.4%
Memory size607.2 KiB
2026-01-14T11:03:14.394504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length475
Median length174
Mean length61.002176
Min length4

Characters and Unicode

Total characters168183
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique812 ?
Unique (%)29.5%

Sample

1st rowCheung, S.K.S.; Liu, X.; Xu, G.; Kwok, L.-F.
2nd rowLiu, M.; Yu, X.; Xu, C.; Song, Y.
3rd rowTammets, K.; Sosnovsky, S.; Ferreira Mello, R.; Pishtari, G.; Nazaretsky, T.
4th rowTammets, K.; Sosnovsky, S.; Ferreira Mello, R.; Pishtari, G.; Nazaretsky, T.
5th rowZhu, T.; Zhou, W.; Zhu, C.
ValueCountFrequency (%)
m1099
 
4.1%
j934
 
3.5%
a795
 
3.0%
s610
 
2.3%
r501
 
1.9%
d494
 
1.8%
c460
 
1.7%
t408
 
1.5%
g351
 
1.3%
p340
 
1.3%
Other values (3938)20745
77.6%
2026-01-14T11:03:14.954364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23980
 
14.3%
.17564
 
10.4%
,13118
 
7.8%
;10428
 
6.2%
a8802
 
5.2%
e6132
 
3.6%
n5910
 
3.5%
i5836
 
3.5%
o5050
 
3.0%
r4916
 
2.9%
Other values (86)66447
39.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)168183
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
23980
 
14.3%
.17564
 
10.4%
,13118
 
7.8%
;10428
 
6.2%
a8802
 
5.2%
e6132
 
3.6%
n5910
 
3.5%
i5836
 
3.5%
o5050
 
3.0%
r4916
 
2.9%
Other values (86)66447
39.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)168183
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
23980
 
14.3%
.17564
 
10.4%
,13118
 
7.8%
;10428
 
6.2%
a8802
 
5.2%
e6132
 
3.6%
n5910
 
3.5%
i5836
 
3.5%
o5050
 
3.0%
r4916
 
2.9%
Other values (86)66447
39.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)168183
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
23980
 
14.3%
.17564
 
10.4%
,13118
 
7.8%
;10428
 
6.2%
a8802
 
5.2%
e6132
 
3.6%
n5910
 
3.5%
i5836
 
3.5%
o5050
 
3.0%
r4916
 
2.9%
Other values (86)66447
39.5%

Publisher
Text

Missing 

Distinct1115
Distinct (%)10.1%
Missing1121
Missing (%)9.2%
Memory size951.9 KiB
2026-01-14T11:03:15.254969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length168
Median length108
Mean length35.738416
Min length3

Characters and Unicode

Total characters395660
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique614 ?
Unique (%)5.5%

Sample

1st rowElsevier Ltd
2nd rowElsevier Ltd
3rd rowElsevier Ltd
4th rowElsevier Ltd
5th rowElsevier Ltd
ValueCountFrequency (%)
and2913
 
5.8%
of2465
 
4.9%
inc2251
 
4.5%
springer1863
 
3.7%
for1860
 
3.7%
institute1674
 
3.4%
association1606
 
3.2%
computing1253
 
2.5%
engineers1252
 
2.5%
machinery1246
 
2.5%
Other values (1852)31476
63.1%
2026-01-14T11:03:15.789801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38788
 
9.8%
i33122
 
8.4%
n32105
 
8.1%
e30929
 
7.8%
c21670
 
5.5%
r21459
 
5.4%
t21037
 
5.3%
o20917
 
5.3%
a20601
 
5.2%
s19859
 
5.0%
Other values (72)135173
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)395660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
38788
 
9.8%
i33122
 
8.4%
n32105
 
8.1%
e30929
 
7.8%
c21670
 
5.5%
r21459
 
5.4%
t21037
 
5.3%
o20917
 
5.3%
a20601
 
5.2%
s19859
 
5.0%
Other values (72)135173
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)395660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
38788
 
9.8%
i33122
 
8.4%
n32105
 
8.1%
e30929
 
7.8%
c21670
 
5.5%
r21459
 
5.4%
t21037
 
5.3%
o20917
 
5.3%
a20601
 
5.2%
s19859
 
5.0%
Other values (72)135173
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)395660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
38788
 
9.8%
i33122
 
8.4%
n32105
 
8.1%
e30929
 
7.8%
c21670
 
5.5%
r21459
 
5.4%
t21037
 
5.3%
o20917
 
5.3%
a20601
 
5.2%
s19859
 
5.0%
Other values (72)135173
34.2%

Sponsors
Text

Missing 

Distinct855
Distinct (%)33.9%
Missing9667
Missing (%)79.3%
Memory size631.9 KiB
2026-01-14T11:03:16.165606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length355
Median length230
Mean length79.124356
Min length3

Characters and Unicode

Total characters199789
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique574 ?
Unique (%)22.7%

Sample

1st rowHong Kong Pei Hua Education Foundation
2nd rowAustralian National University# Google# Monash University# CSIRO-Data61# Pioneer# Yep AI# Australian Computer Society# Defence Artificial Intelligence Research Network (DAIRNET)# Follow Me AI# Computing Research and Education Association of Australasia# UNSW AI Institute# Springer#
3rd rowRedUNCI#National University of La Plata#CIC#CONICET La Plata#National Engineering Academy#PoloITLaPlata
4th rowRedUNCI#National University of La Plata#CIC#CONICET La Plata#National Engineering Academy#PoloITLaPlata
5th rowifip#Sociedade Brasileira de Computacao#FAPEMIG#CNPq#CAPES
ValueCountFrequency (%)
of1065
 
4.1%
acm1022
 
3.9%
ieee890
 
3.4%
society817
 
3.1%
university782
 
3.0%
and740
 
2.8%
education729
 
2.8%
computer645
 
2.5%
sigcse561
 
2.1%
for542
 
2.1%
Other values (2548)18382
70.2%
2026-01-14T11:03:16.686730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23650
 
11.8%
e14673
 
7.3%
n13528
 
6.8%
i13350
 
6.7%
o12525
 
6.3%
t11164
 
5.6%
a9974
 
5.0%
r8231
 
4.1%
c7437
 
3.7%
E6090
 
3.0%
Other values (75)79167
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)199789
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
23650
 
11.8%
e14673
 
7.3%
n13528
 
6.8%
i13350
 
6.7%
o12525
 
6.3%
t11164
 
5.6%
a9974
 
5.0%
r8231
 
4.1%
c7437
 
3.7%
E6090
 
3.0%
Other values (75)79167
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)199789
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
23650
 
11.8%
e14673
 
7.3%
n13528
 
6.8%
i13350
 
6.7%
o12525
 
6.3%
t11164
 
5.6%
a9974
 
5.0%
r8231
 
4.1%
c7437
 
3.7%
E6090
 
3.0%
Other values (75)79167
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)199789
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
23650
 
11.8%
e14673
 
7.3%
n13528
 
6.8%
i13350
 
6.7%
o12525
 
6.3%
t11164
 
5.6%
a9974
 
5.0%
r8231
 
4.1%
c7437
 
3.7%
E6090
 
3.0%
Other values (75)79167
39.6%

Conference name
Text

Missing 

Distinct2734
Distinct (%)48.1%
Missing6503
Missing (%)53.3%
Memory size940.5 KiB
2026-01-14T11:03:17.023173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length407
Median length224
Mean length83.482686
Min length9

Characters and Unicode

Total characters474933
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1890 ?
Unique (%)33.2%

Sample

1st row8th International Conference on Technology in Education, ICTE 2025
2nd row38th Australasian Joint Conference on Artificial Intelligence, AI 2025
3rd row20th European Conference on Technology Enhanced Learning, EC-TEL 2025
4th row20th European Conference on Technology Enhanced Learning, EC-TEL 2025
5th row18th International Conference on Knowledge Science, Engineering and Management, KSEM 2025
ValueCountFrequency (%)
conference4625
 
7.3%
on4132
 
6.6%
international3369
 
5.4%
and2979
 
4.7%
education2215
 
3.5%
in1492
 
2.4%
computer929
 
1.5%
of875
 
1.4%
the864
 
1.4%
science829
 
1.3%
Other values (3112)40659
64.6%
2026-01-14T11:03:17.530885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57279
 
12.1%
n50401
 
10.6%
e36402
 
7.7%
o30492
 
6.4%
t26213
 
5.5%
i25233
 
5.3%
a23901
 
5.0%
r18725
 
3.9%
c16268
 
3.4%
C14105
 
3.0%
Other values (73)175914
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)474933
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
57279
 
12.1%
n50401
 
10.6%
e36402
 
7.7%
o30492
 
6.4%
t26213
 
5.5%
i25233
 
5.3%
a23901
 
5.0%
r18725
 
3.9%
c16268
 
3.4%
C14105
 
3.0%
Other values (73)175914
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)474933
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
57279
 
12.1%
n50401
 
10.6%
e36402
 
7.7%
o30492
 
6.4%
t26213
 
5.5%
i25233
 
5.3%
a23901
 
5.0%
r18725
 
3.9%
c16268
 
3.4%
C14105
 
3.0%
Other values (73)175914
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)474933
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
57279
 
12.1%
n50401
 
10.6%
e36402
 
7.7%
o30492
 
6.4%
t26213
 
5.5%
i25233
 
5.3%
a23901
 
5.0%
r18725
 
3.9%
c16268
 
3.4%
C14105
 
3.0%
Other values (73)175914
37.0%

Conference date
Text

Missing 

Distinct1876
Distinct (%)38.1%
Missing7267
Missing (%)59.6%
Memory size599.3 KiB
2026-01-14T11:03:17.826488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length29
Mean length28.351878
Min length10

Characters and Unicode

Total characters139633
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1085 ?
Unique (%)22.0%

Sample

1st row2025-12-10 through 2025-12-12
2nd row2025-12-01 through 2025-12-05
3rd row2025-09-15 through 2025-09-19
4th row2025-09-15 through 2025-09-19
5th row2025-08-04 through 2025-08-07
ValueCountFrequency (%)
through4757
32.9%
2019-06-1565
 
0.5%
2020-03-1148
 
0.3%
2019-06-1344
 
0.3%
2020-03-1440
 
0.3%
2024-05-3040
 
0.3%
2020-06-1939
 
0.3%
2024-05-2838
 
0.3%
2024-03-2035
 
0.2%
2024-03-2335
 
0.2%
Other values (2399)9298
64.4%
2026-01-14T11:03:18.320474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
022133
15.9%
221870
15.7%
-19364
13.9%
113210
9.5%
h9514
 
6.8%
9514
 
6.8%
t4757
 
3.4%
u4757
 
3.4%
o4757
 
3.4%
r4757
 
3.4%
Other values (8)25000
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)139633
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
022133
15.9%
221870
15.7%
-19364
13.9%
113210
9.5%
h9514
 
6.8%
9514
 
6.8%
t4757
 
3.4%
u4757
 
3.4%
o4757
 
3.4%
r4757
 
3.4%
Other values (8)25000
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)139633
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
022133
15.9%
221870
15.7%
-19364
13.9%
113210
9.5%
h9514
 
6.8%
9514
 
6.8%
t4757
 
3.4%
u4757
 
3.4%
o4757
 
3.4%
r4757
 
3.4%
Other values (8)25000
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)139633
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
022133
15.9%
221870
15.7%
-19364
13.9%
113210
9.5%
h9514
 
6.8%
9514
 
6.8%
t4757
 
3.4%
u4757
 
3.4%
o4757
 
3.4%
r4757
 
3.4%
Other values (8)25000
17.9%

Conference location
Text

Missing 

Distinct875
Distinct (%)17.8%
Missing7265
Missing (%)59.6%
Memory size508.5 KiB
2026-01-14T11:03:18.784508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length27
Mean length9.2689263
Min length3

Characters and Unicode

Total characters45668
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique377 ?
Unique (%)7.7%

Sample

1st rowShenzhen
2nd rowCanberra
3rd rowNewcastle upon Tyne
4th rowNewcastle upon Tyne
5th rowMacao
ValueCountFrequency (%)
virtual664
 
9.9%
online602
 
9.0%
kong176
 
2.6%
hong176
 
2.6%
hybrid141
 
2.1%
portland101
 
1.5%
london72
 
1.1%
city66
 
1.0%
san65
 
1.0%
beijing59
 
0.9%
Other values (877)4584
68.4%
2026-01-14T11:03:19.435663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a4683
 
10.3%
n4399
 
9.6%
i3598
 
7.9%
e2958
 
6.5%
l2927
 
6.4%
o2922
 
6.4%
r2619
 
5.7%
t2081
 
4.6%
u1896
 
4.2%
1779
 
3.9%
Other values (58)15806
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)45668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a4683
 
10.3%
n4399
 
9.6%
i3598
 
7.9%
e2958
 
6.5%
l2927
 
6.4%
o2922
 
6.4%
r2619
 
5.7%
t2081
 
4.6%
u1896
 
4.2%
1779
 
3.9%
Other values (58)15806
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a4683
 
10.3%
n4399
 
9.6%
i3598
 
7.9%
e2958
 
6.5%
l2927
 
6.4%
o2922
 
6.4%
r2619
 
5.7%
t2081
 
4.6%
u1896
 
4.2%
1779
 
3.9%
Other values (58)15806
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a4683
 
10.3%
n4399
 
9.6%
i3598
 
7.9%
e2958
 
6.5%
l2927
 
6.4%
o2922
 
6.4%
r2619
 
5.7%
t2081
 
4.6%
u1896
 
4.2%
1779
 
3.9%
Other values (58)15806
34.6%

Conference code
Real number (ℝ)

High correlation  Missing 

Distinct2215
Distinct (%)45.0%
Missing7267
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean193218.46
Minimum0
Maximum344589
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2026-01-14T11:03:20.465072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile121037
Q1154355
median185019
Q3209971
95-th percentile315263
Maximum344589
Range344589
Interquartile range (IQR)55616

Descriptive statistics

Standard deviation55958.228
Coefficient of variation (CV)0.28961119
Kurtosis0.2795209
Mean193218.46
Median Absolute Deviation (MAD)27925
Skewness0.91663148
Sum9.516009 × 108
Variance3.1313232 × 109
MonotonicityNot monotonic
2026-01-14T11:03:20.669143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24973943
 
0.4%
15796439
 
0.3%
31847938
 
0.3%
19793635
 
0.3%
24971933
 
0.3%
20877332
 
0.3%
24974931
 
0.3%
29008930
 
0.2%
24972929
 
0.2%
14547527
 
0.2%
Other values (2205)4588
37.6%
(Missing)7267
59.6%
ValueCountFrequency (%)
01
< 0.1%
1072821
< 0.1%
1074601
< 0.1%
1077292
< 0.1%
1081171
< 0.1%
1085021
< 0.1%
1085541
< 0.1%
1085931
< 0.1%
1086642
< 0.1%
1087591
< 0.1%
ValueCountFrequency (%)
3445891
< 0.1%
3442391
< 0.1%
3442191
< 0.1%
3437591
< 0.1%
3437191
< 0.1%
3436791
< 0.1%
3435591
< 0.1%
3431091
< 0.1%
3427891
< 0.1%
3426491
< 0.1%

ISSN
Text

Missing 

Distinct1901
Distinct (%)26.2%
Missing4939
Missing (%)40.5%
Memory size561.1 KiB
2026-01-14T11:03:21.142385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length8
Mean length8.4108645
Min length8

Characters and Unicode

Total characters61004
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1214 ?
Unique (%)16.7%

Sample

1st row18711871
2nd row18711871
3rd row18711871
4th row18711871
5th row08936080
ValueCountFrequency (%)
03029743427
 
5.7%
21531633345
 
4.6%
01905848178
 
2.4%
15394565178
 
2.4%
13602357167
 
2.2%
18650929157
 
2.1%
1942647x135
 
1.8%
18149316128
 
1.7%
16130073118
 
1.6%
2367337095
 
1.3%
Other values (1933)5623
74.5%
2026-01-14T11:03:21.805982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18522
14.0%
08434
13.8%
37039
11.5%
26191
10.1%
55349
8.8%
65169
8.5%
95126
8.4%
44878
8.0%
74697
7.7%
84247
7.0%
Other values (3)1352
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)61004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18522
14.0%
08434
13.8%
37039
11.5%
26191
10.1%
55349
8.8%
65169
8.5%
95126
8.4%
44878
8.0%
74697
7.7%
84247
7.0%
Other values (3)1352
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)61004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18522
14.0%
08434
13.8%
37039
11.5%
26191
10.1%
55349
8.8%
65169
8.5%
95126
8.4%
44878
8.0%
74697
7.7%
84247
7.0%
Other values (3)1352
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)61004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18522
14.0%
08434
13.8%
37039
11.5%
26191
10.1%
55349
8.8%
65169
8.5%
95126
8.4%
44878
8.0%
74697
7.7%
84247
7.0%
Other values (3)1352
 
2.2%

ISBN
Text

Missing 

Distinct2078
Distinct (%)31.5%
Missing5604
Missing (%)46.0%
Memory size962.6 KiB
2026-01-14T11:03:22.096017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length148
Median length145
Mean length73.384942
Min length10

Characters and Unicode

Total characters483460
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1487 ?
Unique (%)22.6%

Sample

1st row008030270X; 9780080302706
2nd row008031418X; 0080323723; 9780080323725
3rd row9781394352821; 9781394352814
4th row9789819671748; 9789819664610; 9783032026743; 9783032008831; 9783032026712; 9789819671779; 9783031949425; 9789819666874; 9783031936968; 9783031941207
5th row9789819698936; 9789819698042; 9789819698110; 9789819698905; 9783032004949; 9789819512324; 9783032026019; 9783032008909; 9783031915802; 9789819698141
ValueCountFrequency (%)
9789819698936427
 
1.3%
9789819698042427
 
1.3%
9789819698905427
 
1.3%
9789819698110427
 
1.3%
9789819698141427
 
1.3%
9789819512324427
 
1.3%
9783032026019427
 
1.3%
9783032008909427
 
1.3%
9783032004949427
 
1.3%
9783031915802427
 
1.3%
Other values (5010)29313
87.3%
2026-01-14T11:03:22.457426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
971497
14.8%
862725
13.0%
757715
11.9%
045863
9.5%
143544
9.0%
341773
8.6%
431717
6.6%
26995
 
5.6%
;26994
 
5.6%
526077
 
5.4%
Other values (3)48560
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)483460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
971497
14.8%
862725
13.0%
757715
11.9%
045863
9.5%
143544
9.0%
341773
8.6%
431717
6.6%
26995
 
5.6%
;26994
 
5.6%
526077
 
5.4%
Other values (3)48560
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)483460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
971497
14.8%
862725
13.0%
757715
11.9%
045863
9.5%
143544
9.0%
341773
8.6%
431717
6.6%
26995
 
5.6%
;26994
 
5.6%
526077
 
5.4%
Other values (3)48560
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)483460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
971497
14.8%
862725
13.0%
757715
11.9%
045863
9.5%
143544
9.0%
341773
8.6%
431717
6.6%
26995
 
5.6%
;26994
 
5.6%
526077
 
5.4%
Other values (3)48560
10.0%

CODEN
Text

Missing 

Distinct599
Distinct (%)35.5%
Missing10506
Missing (%)86.2%
Memory size417.4 KiB
2026-01-14T11:03:22.824792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters8430
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique373 ?
Unique (%)22.1%

Sample

1st rowNNETE
2nd rowCOMED
3rd rowNNETE
4th rowMIAEC
5th rowCCEND
ValueCountFrequency (%)
pfecd178
 
10.6%
comed63
 
3.7%
capee49
 
2.9%
cacma39
 
2.3%
icmlf35
 
2.1%
psisd28
 
1.7%
chbee27
 
1.6%
bjetd27
 
1.6%
tcscf24
 
1.4%
cgtna23
 
1.4%
Other values (589)1193
70.8%
2026-01-14T11:03:23.315698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E1233
14.6%
C1043
12.4%
D724
 
8.6%
A723
 
8.6%
P565
 
6.7%
S521
 
6.2%
I485
 
5.8%
F406
 
4.8%
M380
 
4.5%
B336
 
4.0%
Other values (26)2014
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)8430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E1233
14.6%
C1043
12.4%
D724
 
8.6%
A723
 
8.6%
P565
 
6.7%
S521
 
6.2%
I485
 
5.8%
F406
 
4.8%
M380
 
4.5%
B336
 
4.0%
Other values (26)2014
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E1233
14.6%
C1043
12.4%
D724
 
8.6%
A723
 
8.6%
P565
 
6.7%
S521
 
6.2%
I485
 
5.8%
F406
 
4.8%
M380
 
4.5%
B336
 
4.0%
Other values (26)2014
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E1233
14.6%
C1043
12.4%
D724
 
8.6%
A723
 
8.6%
P565
 
6.7%
S521
 
6.2%
I485
 
5.8%
F406
 
4.8%
M380
 
4.5%
B336
 
4.0%
Other values (26)2014
23.9%

PubMed ID
Real number (ℝ)

High correlation  Missing 

Distinct664
Distinct (%)100.0%
Missing11528
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean29006954
Minimum1488649
Maximum41337481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2026-01-14T11:03:23.467181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1488649
5-th percentile12537762
Q123593944
median30134232
Q335461219
95-th percentile40299276
Maximum41337481
Range39848832
Interquartile range (IQR)11867276

Descriptive statistics

Standard deviation8215618
Coefficient of variation (CV)0.28322926
Kurtosis-0.073074655
Mean29006954
Median Absolute Deviation (MAD)5853908
Skewness-0.65109785
Sum1.9260617 × 1010
Variance6.7496379 × 1013
MonotonicityNot monotonic
2026-01-14T11:03:23.613641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
259001341
 
< 0.1%
260415801
 
< 0.1%
258246711
 
< 0.1%
257211051
 
< 0.1%
259024731
 
< 0.1%
258800641
 
< 0.1%
257721591
 
< 0.1%
257269191
 
< 0.1%
258988071
 
< 0.1%
258679961
 
< 0.1%
Other values (654)654
 
5.4%
(Missing)11528
94.6%
ValueCountFrequency (%)
14886491
< 0.1%
15461141
< 0.1%
65431651
< 0.1%
66452141
< 0.1%
75011301
< 0.1%
77245671
< 0.1%
80229561
< 0.1%
82620291
< 0.1%
83645331
< 0.1%
83740681
< 0.1%
ValueCountFrequency (%)
413374811
< 0.1%
413155441
< 0.1%
413052651
< 0.1%
412049761
< 0.1%
410941171
< 0.1%
410533581
< 0.1%
410471561
< 0.1%
410431411
< 0.1%
410203631
< 0.1%
410065371
< 0.1%

Language of Original Document
Categorical

Imbalance 

Distinct17
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size667.0 KiB
English
11764 
Spanish
 
159
Chinese
 
149
Portuguese
 
50
Russian
 
24
Other values (12)
 
45

Length

Max length10
Median length7
Mean length7.0113198
Min length6

Characters and Unicode

Total characters85475
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English11764
96.5%
Spanish159
 
1.3%
Chinese149
 
1.2%
Portuguese50
 
0.4%
Russian24
 
0.2%
Italian12
 
0.1%
German9
 
0.1%
Polish4
 
< 0.1%
Japanese4
 
< 0.1%
Croatian3
 
< 0.1%
Other values (7)13
 
0.1%

Length

2026-01-14T11:03:23.750670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english11764
96.5%
spanish159
 
1.3%
chinese149
 
1.2%
portuguese50
 
0.4%
russian24
 
0.2%
italian12
 
0.1%
german9
 
0.1%
polish4
 
< 0.1%
japanese4
 
< 0.1%
croatian3
 
< 0.1%
Other values (7)13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
s12182
14.3%
n12132
14.2%
i12123
14.2%
h12082
14.1%
g11815
13.8%
l11780
13.8%
E11764
13.8%
e422
 
0.5%
a238
 
0.3%
p163
 
0.2%
Other values (22)774
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)85475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s12182
14.3%
n12132
14.2%
i12123
14.2%
h12082
14.1%
g11815
13.8%
l11780
13.8%
E11764
13.8%
e422
 
0.5%
a238
 
0.3%
p163
 
0.2%
Other values (22)774
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)85475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s12182
14.3%
n12132
14.2%
i12123
14.2%
h12082
14.1%
g11815
13.8%
l11780
13.8%
E11764
13.8%
e422
 
0.5%
a238
 
0.3%
p163
 
0.2%
Other values (22)774
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)85475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s12182
14.3%
n12132
14.2%
i12123
14.2%
h12082
14.1%
g11815
13.8%
l11780
13.8%
E11764
13.8%
e422
 
0.5%
a238
 
0.3%
p163
 
0.2%
Other values (22)774
 
0.9%
Distinct3880
Distinct (%)31.8%
Missing7
Missing (%)0.1%
Memory size962.3 KiB
2026-01-14T11:03:24.113145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length253
Median length126
Mean length31.761182
Min length3

Characters and Unicode

Total characters387010
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2635 ?
Unique (%)21.6%

Sample

1st rowThink. Skills Creat.
2nd rowThink. Skills Creat.
3rd rowThink. Skills Creat.
4th rowThink. Skills Creat.
5th rowNeural Netw.
ValueCountFrequency (%)
conf4005
 
6.3%
proc3715
 
5.9%
educ3246
 
5.1%
comput3218
 
5.1%
int2936
 
4.6%
sci2349
 
3.7%
j1773
 
2.8%
1572
 
2.5%
technol1383
 
2.2%
acm1083
 
1.7%
Other values (3800)37917
60.0%
2026-01-14T11:03:24.638793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51012
 
13.2%
.40354
 
10.4%
o24721
 
6.4%
n23542
 
6.1%
t17652
 
4.6%
c17231
 
4.5%
e16524
 
4.3%
C14082
 
3.6%
r13548
 
3.5%
i12241
 
3.2%
Other values (75)156103
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)387010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
51012
 
13.2%
.40354
 
10.4%
o24721
 
6.4%
n23542
 
6.1%
t17652
 
4.6%
c17231
 
4.5%
e16524
 
4.3%
C14082
 
3.6%
r13548
 
3.5%
i12241
 
3.2%
Other values (75)156103
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)387010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
51012
 
13.2%
.40354
 
10.4%
o24721
 
6.4%
n23542
 
6.1%
t17652
 
4.6%
c17231
 
4.5%
e16524
 
4.3%
C14082
 
3.6%
r13548
 
3.5%
i12241
 
3.2%
Other values (75)156103
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)387010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
51012
 
13.2%
.40354
 
10.4%
o24721
 
6.4%
n23542
 
6.1%
t17652
 
4.6%
c17231
 
4.5%
e16524
 
4.3%
C14082
 
3.6%
r13548
 
3.5%
i12241
 
3.2%
Other values (75)156103
40.3%

Document Type
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size720.7 KiB
Conference paper
5387 
Article
4739 
Book chapter
847 
Review
 
471
Conference review
 
350
Other values (8)
 
398

Length

Max length17
Median length16
Mean length11.524524
Min length4

Characters and Unicode

Total characters140507
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArticle
2nd rowArticle
3rd rowArticle
4th rowArticle
5th rowArticle

Common Values

ValueCountFrequency (%)
Conference paper5387
44.2%
Article4739
38.9%
Book chapter847
 
6.9%
Review471
 
3.9%
Conference review350
 
2.9%
Book221
 
1.8%
Editorial54
 
0.4%
Note49
 
0.4%
Erratum28
 
0.2%
Short survey21
 
0.2%
Other values (3)25
 
0.2%

Length

2026-01-14T11:03:24.768121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
conference5737
30.5%
paper5389
28.7%
article4739
25.2%
book1068
 
5.7%
chapter847
 
4.5%
review821
 
4.4%
editorial54
 
0.3%
note49
 
0.3%
erratum28
 
0.1%
short21
 
0.1%
Other values (4)46
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e29944
21.3%
r17237
12.3%
p11625
 
8.3%
n11474
 
8.2%
c11333
 
8.1%
o7997
 
5.7%
6607
 
4.7%
a6332
 
4.5%
t5786
 
4.1%
C5737
 
4.1%
Other values (20)26435
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)140507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e29944
21.3%
r17237
12.3%
p11625
 
8.3%
n11474
 
8.2%
c11333
 
8.1%
o7997
 
5.7%
6607
 
4.7%
a6332
 
4.5%
t5786
 
4.1%
C5737
 
4.1%
Other values (20)26435
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)140507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e29944
21.3%
r17237
12.3%
p11625
 
8.3%
n11474
 
8.2%
c11333
 
8.1%
o7997
 
5.7%
6607
 
4.7%
a6332
 
4.5%
t5786
 
4.1%
C5737
 
4.1%
Other values (20)26435
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)140507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e29944
21.3%
r17237
12.3%
p11625
 
8.3%
n11474
 
8.2%
c11333
 
8.1%
o7997
 
5.7%
6607
 
4.7%
a6332
 
4.5%
t5786
 
4.1%
C5737
 
4.1%
Other values (20)26435
18.8%

Publication Stage
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size642.8 KiB
Final
12076 
aip
 
116

Length

Max length5
Median length5
Mean length4.9809711
Min length3

Characters and Unicode

Total characters60728
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinal
2nd rowFinal
3rd rowFinal
4th rowFinal
5th rowFinal

Common Values

ValueCountFrequency (%)
Final12076
99.0%
aip116
 
1.0%

Length

2026-01-14T11:03:24.886147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T11:03:24.960962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
final12076
99.0%
aip116
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i12192
20.1%
a12192
20.1%
F12076
19.9%
n12076
19.9%
l12076
19.9%
p116
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)60728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i12192
20.1%
a12192
20.1%
F12076
19.9%
n12076
19.9%
l12076
19.9%
p116
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i12192
20.1%
a12192
20.1%
F12076
19.9%
n12076
19.9%
l12076
19.9%
p116
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i12192
20.1%
a12192
20.1%
F12076
19.9%
n12076
19.9%
l12076
19.9%
p116
 
0.2%

Open Access
Categorical

Missing 

Distinct20
Distinct (%)0.6%
Missing8657
Missing (%)71.0%
Memory size823.8 KiB
All Open Access; Gold Open Access
1388 
All Open Access; Gold Open Access; Green Accepted Open Access; Green Open Access
690 
All Open Access; Hybrid Gold Open Access
383 
All Open Access; Green Accepted Open Access; Green Open Access
305 
All Open Access; Bronze Open Access
274 
Other values (15)
495 

Length

Max length112
Median length107
Mean length52.461103
Min length15

Characters and Unicode

Total characters185450
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowAll Open Access; Hybrid Gold Open Access
2nd rowAll Open Access; Hybrid Gold Open Access
3rd rowAll Open Access; Hybrid Gold Open Access
4th rowAll Open Access; Hybrid Gold Open Access
5th rowAll Open Access; Gold Open Access

Common Values

ValueCountFrequency (%)
All Open Access; Gold Open Access1388
 
11.4%
All Open Access; Gold Open Access; Green Accepted Open Access; Green Open Access690
 
5.7%
All Open Access; Hybrid Gold Open Access383
 
3.1%
All Open Access; Green Accepted Open Access; Green Open Access305
 
2.5%
All Open Access; Bronze Open Access274
 
2.2%
All Open Access; Green Accepted Open Access; Green Open Access; Hybrid Gold Open Access177
 
1.5%
All Open Access; Gold Open Access; Green Final Open Access; Green Open Access76
 
0.6%
All Open Access; Bronze Open Access; Green Accepted Open Access; Green Open Access76
 
0.6%
All Open Access; Green Final Open Access; Green Open Access44
 
0.4%
All Open Access; Gold Open Access; Green Accepted Open Access; Green Final Open Access; Green Open Access35
 
0.3%
Other values (10)87
 
0.7%
(Missing)8657
71.0%

Length

2026-01-14T11:03:25.053439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
open9718
31.1%
access9718
31.1%
all3535
 
11.3%
green3021
 
9.7%
gold2791
 
8.9%
accepted1311
 
4.2%
hybrid601
 
1.9%
bronze371
 
1.2%
final227
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e28471
15.4%
27758
15.0%
c22058
11.9%
s19436
10.5%
A14564
7.9%
n13337
7.2%
p11029
 
5.9%
l10088
 
5.4%
O9718
 
5.2%
;6183
 
3.3%
Other values (13)22808
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)185450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e28471
15.4%
27758
15.0%
c22058
11.9%
s19436
10.5%
A14564
7.9%
n13337
7.2%
p11029
 
5.9%
l10088
 
5.4%
O9718
 
5.2%
;6183
 
3.3%
Other values (13)22808
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)185450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e28471
15.4%
27758
15.0%
c22058
11.9%
s19436
10.5%
A14564
7.9%
n13337
7.2%
p11029
 
5.9%
l10088
 
5.4%
O9718
 
5.2%
;6183
 
3.3%
Other values (13)22808
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)185450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e28471
15.4%
27758
15.0%
c22058
11.9%
s19436
10.5%
A14564
7.9%
n13337
7.2%
p11029
 
5.9%
l10088
 
5.4%
O9718
 
5.2%
;6183
 
3.3%
Other values (13)22808
12.3%

Source
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.0 KiB
Scopus
12192 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters73152
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScopus
2nd rowScopus
3rd rowScopus
4th rowScopus
5th rowScopus

Common Values

ValueCountFrequency (%)
Scopus12192
100.0%

Length

2026-01-14T11:03:25.160511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T11:03:25.238855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
scopus12192
100.0%

Most occurring characters

ValueCountFrequency (%)
S12192
16.7%
c12192
16.7%
o12192
16.7%
p12192
16.7%
u12192
16.7%
s12192
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)73152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S12192
16.7%
c12192
16.7%
o12192
16.7%
p12192
16.7%
u12192
16.7%
s12192
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)73152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S12192
16.7%
c12192
16.7%
o12192
16.7%
p12192
16.7%
u12192
16.7%
s12192
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)73152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S12192
16.7%
c12192
16.7%
o12192
16.7%
p12192
16.7%
u12192
16.7%
s12192
16.7%

EID
Text

Unique 

Distinct12192
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size798.9 KiB
2026-01-14T11:03:25.419392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length18
Mean length18.088583
Min length17

Characters and Unicode

Total characters220536
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12192 ?
Unique (%)100.0%

Sample

1st row2-s2.0-105023692746
2nd row2-s2.0-105022798679
3rd row2-s2.0-105021925885
4th row2-s2.0-105021238108
5th row2-s2.0-105025196185
ValueCountFrequency (%)
2-s2.0-00217066711
 
< 0.1%
2-s2.0-00217000411
 
< 0.1%
2-s2.0-00215397131
 
< 0.1%
2-s2.0-00215263511
 
< 0.1%
2-s2.0-00214970591
 
< 0.1%
2-s2.0-851195029871
 
< 0.1%
2-s2.0-676504260741
 
< 0.1%
2-s2.0-28425943311
 
< 0.1%
2-s2.0-00208377971
 
< 0.1%
2-s2.0-00208047761
 
< 0.1%
Other values (12182)12182
99.9%
2026-01-14T11:03:25.766713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
235201
16.0%
029172
13.2%
-24384
11.1%
820166
9.1%
519327
8.8%
115988
7.2%
s12192
 
5.5%
.12192
 
5.5%
411535
 
5.2%
911111
 
5.0%
Other values (3)29268
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)220536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
235201
16.0%
029172
13.2%
-24384
11.1%
820166
9.1%
519327
8.8%
115988
7.2%
s12192
 
5.5%
.12192
 
5.5%
411535
 
5.2%
911111
 
5.0%
Other values (3)29268
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)220536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
235201
16.0%
029172
13.2%
-24384
11.1%
820166
9.1%
519327
8.8%
115988
7.2%
s12192
 
5.5%
.12192
 
5.5%
411535
 
5.2%
911111
 
5.0%
Other values (3)29268
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)220536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
235201
16.0%
029172
13.2%
-24384
11.1%
820166
9.1%
519327
8.8%
115988
7.2%
s12192
 
5.5%
.12192
 
5.5%
411535
 
5.2%
911111
 
5.0%
Other values (3)29268
13.3%

doi_norm
Text

Missing 

Distinct10106
Distinct (%)99.8%
Missing2068
Missing (%)17.0%
Memory size801.5 KiB
2026-01-14T11:03:26.040200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length66
Median length58
Mean length25.522916
Min length12

Characters and Unicode

Total characters258394
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10088 ?
Unique (%)99.6%

Sample

1st row10.1016/j.tsc.2025.102068
2nd row10.1016/j.tsc.2025.102070
3rd row10.1016/j.tsc.2025.102056
4th row10.1016/j.tsc.2025.102049
5th row10.1016/j.neunet.2025.108407
ValueCountFrequency (%)
10.1007/s11423-023-10328-82
 
< 0.1%
10.1051/e3sconf/2024538050342
 
< 0.1%
10.1145/1140124.11401612
 
< 0.1%
10.1016/j.procir.2024.10.1612
 
< 0.1%
10.1145/3159450.31595862
 
< 0.1%
10.1016/b978-0-12-809324-5.23765-62
 
< 0.1%
10.1016/b978-044451719-7/50072-x2
 
< 0.1%
10.1016/b978-0-12-804071-3.00012-42
 
< 0.1%
10.4324/97813512323572
 
< 0.1%
10.34190/gbl.20.1562
 
< 0.1%
Other values (10096)10104
99.8%
2026-01-14T11:03:26.507255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
141207
15.9%
040154
15.5%
.22455
 
8.7%
218979
 
7.3%
315621
 
6.0%
913345
 
5.2%
712030
 
4.7%
411597
 
4.5%
511450
 
4.4%
811024
 
4.3%
Other values (38)60532
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)258394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
141207
15.9%
040154
15.5%
.22455
 
8.7%
218979
 
7.3%
315621
 
6.0%
913345
 
5.2%
712030
 
4.7%
411597
 
4.5%
511450
 
4.4%
811024
 
4.3%
Other values (38)60532
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)258394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
141207
15.9%
040154
15.5%
.22455
 
8.7%
218979
 
7.3%
315621
 
6.0%
913345
 
5.2%
712030
 
4.7%
411597
 
4.5%
511450
 
4.4%
811024
 
4.3%
Other values (38)60532
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)258394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
141207
15.9%
040154
15.5%
.22455
 
8.7%
218979
 
7.3%
315621
 
6.0%
913345
 
5.2%
712030
 
4.7%
411597
 
4.5%
511450
 
4.4%
811024
 
4.3%
Other values (38)60532
23.4%

Interactions

2026-01-14T11:02:45.606699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:43.988119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:44.521215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:45.019414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:45.724369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:44.123148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:44.657367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:45.159994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:45.855116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:44.260698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:44.764745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:45.338667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:45.963025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:44.399684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:44.891386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:45.502000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-14T11:03:26.608854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Cited byConference codeDocument TypeLanguage of Original DocumentOpen AccessPubMed IDPublication StageYear
Cited by1.000-0.4180.0350.0000.056-0.4570.000-0.360
Conference code-0.4181.0000.0410.0940.1771.0001.0000.727
Document Type0.0350.0411.0000.0210.1180.0500.1130.066
Language of Original Document0.0000.0940.0211.0000.0000.2510.0000.027
Open Access0.0560.1770.1180.0001.0000.1560.1410.140
PubMed ID-0.4571.0000.0500.2510.1561.0000.0410.996
Publication Stage0.0001.0000.1130.0000.1410.0411.0000.087
Year-0.3600.7270.0660.0270.1400.9960.0871.000

Missing values

2026-01-14T11:02:46.303001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-14T11:02:46.759390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-14T11:02:47.636341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AuthorsAuthor full namesAuthor(s) IDTitleYearSource titleVolumeIssueArt. No.Page startPage endCited byDOILinkAffiliationsAuthors with affiliationsAbstractAuthor KeywordsIndex KeywordsMolecular Sequence NumbersChemicals/CASTradenamesManufacturersFunding DetailsFunding TextsReferencesCorrespondence AddressEditorsPublisherSponsorsConference nameConference dateConference locationConference codeISSNISBNCODENPubMed IDLanguage of Original DocumentAbbreviated Source TitleDocument TypePublication StageOpen AccessSourceEIDdoi_norm
0Wang, Y.Wang, Yang (57208730125)57208730125Effects of troubleshooting robotics learning on students’ engagement, computational thinking, and programming skills2026Thinking Skills and Creativity60NaN102068NaNNaN010.1016/j.tsc.2025.102068https://www.scopus.com/inward/record.uri?eid=2-s2.0-105023692746&doi=10.1016%2Fj.tsc.2025.102068&partnerID=40&md5=a8d0b7d575d39357a9ab8fabecdc6c4eNanjing Normal University, Nanjing, Jiangsu, ChinaWang, Yang, Nanjing Normal University, Nanjing, Jiangsu, ChinaLearning engagement is an important indicator of active learning outcomes. Computational thinking is a basic competency required in the 21st century. Troubleshooting learning is helpful to enhance students’ computational thinking and engagement, as its targeted error analysis addresses traditional learning’s limitation of insufficient guidance on error-prone points. However, the role of troubleshooting in students’ engagement and computational thinking in robotics programming learning is to be explored. To fill in this gap, the current study explored the effects of troubleshooting robotics programming learning on students’ engagement, computational thinking, and programming skills. A quasi-experimental study was conducted to explore the effects of troubleshooting learning on students’ robotics programming learning by comparing students’ learning results in two courses instructed by the same instructor (one instructed with a problem-based method, the other instructed with a troubleshooting method). The participants were seventy-nine students from a university in China. Questionnaires, tests, and work analyses were used to measure students’ engagement, computational thinking, and programming skills. The results indicated that troubleshooting learning is more effective in enhancing students’ engagement (i.e., behavioral, cognitive, and emotional engagement), computational thinking (i.e., cooperativity, critical thinking, and creativity) and programming learning (i.e., data representation). The findings provide insight into troubleshooting-supported robotics programming learning. Different types of troubleshooting tasks with progressive difficulty are effective in enhancing students’ learning. Troubleshooting could be used in the early stages of programming learning to help students master the error prone areas of programming. © 2025 Elsevier Ltd.Computational thinking; Programming skills; Robotics programming learning; TroubleshootingNaNNaNNaNNaNNaNMinistry of Education, MOE; Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province, (25JYC004)This work was supported by the Project of Humanities and Social Sciences Program of the Ministry of Education , the Philosophy and Social Science Research project of Jiangsu province (No. 25JYC004 ).Astin, Alexander W., Student involvement: A developmental theory for higher education, Journal of College Student Development, 40, 5, pp. 518-529, (1999); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); Bacca, Jorge, Student engagement with mobile-based assessment systems: A survival analysis, Journal of Computer Assisted Learning, 37, 1, pp. 158-171, (2021); Melander Bowden, Helen, Problem-solving in collaborative game design practices: epistemic stance, affect, and engagement, Learning, Media and Technology, 44, 2, pp. 124-143, (2019); APA Handbook of Research Methods in Psychology Research Designs Quantitative Qualitative Neuropsychological and Biological, (2023); Buil, Isabel, Engagement in business simulation games: A self-system model of motivational development, British Journal of Educational Technology, 51, 1, pp. 297-311, (2020); Çakır, Recep, The effect of robotic coding education on preschoolers’ problem solving and creative thinking skills, Thinking Skills and Creativity, 40, (2021); Thinking Skills and Creativity, (2021); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); undefinedY. Wang; Adolescent Education and Intelligence Support Lab of Nanjing Normal University, Nanjing, China; email: wangyang@nnu.edu.cnNaNElsevier LtdNaNNaNNaNNaNNaN18711871NaNNaNNaNEnglishThink. Skills Creat.ArticleFinalNaNScopus2-s2.0-10502369274610.1016/j.tsc.2025.102068
1Lin, Y.; Zhang, Y.; Yang, Y.; Pan, S.; Ren, X.; Chen, D.Lin, Yuru (57281795200); Zhang, Yi (58957195500); Yang, Yuqin (57164390600); Pan, Shidan (60209651800); Ren, Xu (60209651900); Chen, Dengkang (57898076100)57281795200; 58957195500; 57164390600; 60209651800; 60209651900; 57898076100Facilitating computational thinking with AI: A three-level meta-analytic evidence for future-ready learning2026Thinking Skills and Creativity60NaN102070NaNNaN010.1016/j.tsc.2025.102070https://www.scopus.com/inward/record.uri?eid=2-s2.0-105022798679&doi=10.1016%2Fj.tsc.2025.102070&partnerID=40&md5=420bda068ce007f4339494c4fa7783c7Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, ChinaLin, Yuru, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Zhang, Yi, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Yang, Yuqin, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Pan, Shidan, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Ren, Xu, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Chen, Dengkang, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, ChinaThe integration of artificial intelligence (AI) tools in education to promote computational thinking (CT) among students has become a trending topic of research; however, there is no consensus on the impact of such tools on CT. Qualitative syntheses regarding both the effect of AI tools and how to unleash their power more effectively are also lacking. Using a three-level meta-analytic approach, this study evaluated the effectiveness of AI tools in improving students’ CT and investigated the various moderating variables. A total of 32 empirical studies with 44 effect sizes were included in this meta-analysis, and the results showed that AI tools have a significant and moderately large effect on students’ CT (Hedges’s g = 0.75, 95 % CI [0.55, 0.95], p < 0.0001). Moderator analyses revealed that AI technologies, the application of AI tools, as well as tool customization and its method, and sample size significantly influence the effectiveness of AI tools. Other moderators—including region, publication year, subject disciplines, instructional approach, collaboration type, intervention duration, gender, and educational level—appeared to be universally effective in promoting student CT. Overall, this meta-analysis contributes to both the academic understanding and practical application of AI tools in CT education to help students prepare for the smart society of the future. © 2025 Elsevier Ltd.Artificial intelligence; Artificial intelligence in education; Computational thinking; Moderator analysis; Three-level meta-analysisNaNNaNNaNNaNNaNNational Natural Science Foundation of China, NSFC, (72274076); Fundamental Research Funds for the Central Universities, (30106250032)This study was funded by the 2023 National Natural Science Foundation of China (Grant No. 72274076) and funded by the Fundamental Research Funds for the Central Universities (Outstanding Innovation Project, No. 30106250032).Aldabe, Itziar, Semantic similarity measures for the generation of science tests in basque, IEEE Transactions on Learning Technologies, 7, 4, pp. 375-387, (2014); Ameen, Linda Talib, The Impact of Artificial Intelligence on Computational Thinking in Education at University, International Journal of Engineering Pedagogy, 14, 5, pp. 192-203, (2024); Angeli Valanides, Charoula Nicos, Investigating the effects of gender and scaffolding in developing preschool children’s computational thinking during problem-solving with Bee-Bots, Frontiers in Education, 7, (2023); Asunda, Paul A., Embracing Computational Thinking as an Impetus for Artificial Intelligence in Integrated STEM Disciplines through Engineering and Technology Education, Journal of Technology Education, 34, 2, pp. 43-63, (2023); Atkinson, Richard C., Human Memory: A Proposed System and its Control Processes, Psychology of Learning and Motivation - Advances in Research and Theory, 2, C, pp. 89-195, (1968); Jbi Manual for Evidence Synthesis, (2024); Educ AI Tion Rebooted Exploring the Future of Artificial Intelligence in Schools and Colleges, (2019); Basu, Satabdi, Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment, User Modeling and User-Adapted Interaction, 27, 1, pp. 5-53, (2017); Bhatt, Sohum Mandar, A Method for Developing Process-Based Assessments for Computational Thinking Tasks, Journal of Learning Analytics, 11, 2, pp. 157-173, (2024); Belland, Brian R., A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education, Review of Educational Research, 87, 6, pp. 1042-1081, (2017)Y. Yang; Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, No. 152 Luoyu Road, Hubei, 430079, China; email: yangyuqin@ccnu.edu.cnNaNElsevier LtdNaNNaNNaNNaNNaN18711871NaNNaNNaNEnglishThink. Skills Creat.ArticleFinalNaNScopus2-s2.0-10502279867910.1016/j.tsc.2025.102070
2Hsu, T.-C.; Hsu, T.-P.Hsu, Tingchia (35173046500); Hsu, Taiping (58366049000)35173046500; 58366049000Effects of game-based learning integrated with different thinking-guided methods on computational thinking of elementary school students2026Thinking Skills and Creativity60NaN102056NaNNaN010.1016/j.tsc.2025.102056https://www.scopus.com/inward/record.uri?eid=2-s2.0-105021925885&doi=10.1016%2Fj.tsc.2025.102056&partnerID=40&md5=11dec6c981e61a08f0343592d5fbed9fDepartment of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, TaiwanHsu, Tingchia, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan; Hsu, Taiping, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, TaiwanThe study developed an online game system for young students to learn computational thinking (CT), and explored the CT learning achievements and self-efficacy of students using two thinking-guided methods. One method was 5W1H, which is well known in science learning, and the other was concept-association-based concept mapping (CABCM). These thinking-guided methods, aimed at the beginning stage of problem analysis, were utilized before playing the online game, with the aim of helping students learn and solve CT tasks in the game scenarios. The research involved 54 students whose average age was 10, divided into two groups based on the different thinking-guided methods. The experimental results showed that students in both the CABCM and 5W1H groups demonstrated significant learning gains in CT achievement and self-efficacy from pre-test to post-test. While no statistically significant difference was found in the post-test scores between the two groups, a detailed analysis of learning behaviors revealed distinct problem-solving pathways associated with each thinking-guided method. The findings suggest that both integrated approaches effectively fostered CT skills, albeit through different cognitive processes. This research contributes to CT education by integrating thinking-guided methods into an online CT game. It offers empirical evidence on the effectiveness of such integrated approaches and provides insights into the processes and behaviors associated with different thinking-guided methods, shedding light on students' challenges in learning CT through games. © © 2025. Published by Elsevier Ltd.5W1H; Computational thinking; Concept-association-based concept mapping strategy; Self-efficacyNaNNaNNaNNaNNaN(NSTC 111-2410-H-003-168-MY3)This study is supported in part by the National Science and Technology Council in the Republic of China under contract numbers NSTC 111-2410-H-003-168-MY3 .Alsadoon, Elham, Effects of a gamified learning environment on students’ achievement, motivations, and satisfaction, Heliyon, 8, 8, (2022); Journal of Languages and Language Teaching, (2023); Avcı, Canan, Computational thinking: early childhood teachers’ and prospective teachers’ preconceptions and self-efficacy, Education and Information Technologies, 27, 8, pp. 11689-11713, (2022); Bakeman, Roger A., Observer agreement for timed-event sequential data: A comparison of time-based and event-based algorithms, Behavior Research Methods, 41, 1, pp. 137-147, (2009); Bers, Marina Umaschi, Computational thinking and tinkering: Exploration of an early childhood robotics curriculum, Computers and Education, 72, pp. 145-157, (2014); Annual American Educational Research Association Meeting, (2012); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); Cheng, Shuchen, Facilitating creativity, collaboration, and computational thinking in group website design: a concept mapping-based mobile flipped learning approach, International Journal of Mobile Learning and Organisation, 18, 2, pp. 169-193, (2024); Cheng, Yuping, Enhancing student's computational thinking skills with student-generated questions strategy in a game-based learning platform, Computers and Education, 200, (2023); Chevalier, Morgane, The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school, Computers and Education, 180, (2022)T.-P. Hsu; Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei city, 162, Sec. 1, East Heping Rd, 10610, Taiwan; email: 81171002H@ntnu.edu.twNaNElsevier LtdNaNNaNNaNNaNNaN18711871NaNNaNNaNEnglishThink. Skills Creat.ArticleFinalNaNScopus2-s2.0-10502192588510.1016/j.tsc.2025.102056
3Aksoy, B.D.; Mumcu, F.K.; Cantürk Günhan, B.C.Aksoy, Behiye Dinçer (60177502400); Mumcu, Filiz Kuşkaya (13410584100); Cantürk Günhan, Berna (36815607700)60177502400; 13410584100; 36815607700Unveiling the nexus: Computational thinking and mathematical modelling in K-12 education- a teacher-centric exploration2026Thinking Skills and Creativity60NaN102049NaNNaN010.1016/j.tsc.2025.102049https://www.scopus.com/inward/record.uri?eid=2-s2.0-105021238108&doi=10.1016%2Fj.tsc.2025.102049&partnerID=40&md5=6a9d8142edba11229cdd741ebbab5ecdGeneral Directorate of Innovation and Educational Technologies, Ankara, Turkey; Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, TurkeyAksoy, Behiye Dinçer, General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Mumcu, Filiz Kuşkaya, Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Cantürk Günhan, Berna, Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, TurkeyThis study explores how Computational Thinking (CT) components overlap with the phases of mathematical modelling within the context of a Teacher Development Course (TDC). The course was designed, developed, implemented, and assessed to enhance teachers’ cognitive actions in integrating CT with mathematical modelling. This research study was conducted with three mathematics teachers and one computer science teacher. Data were collected through CT component worksheets and video recordings, and analysed based on Borromeo-Ferri’s (2006) modelling cycle and the study’s CT framework. The study’s findings indicate that modelling processes enhanced teachers’ CT skills, while CT components made the modelling process more structured and reflective, revealing a reciprocal relationship between modelling and CT. The study proposes an original interdisciplinary framework linking teachers’ cognitive actions to CT integration, offering both theoretical and practical contributions. © 2025 The Author(s).Computational thinking; CT components; CT-integrated maths education; Mathematical modelling; Teacher developmentNaNNaNNaNNaNNaNNaNNaNTurkish Studies Educational Sciences, (2020); Mathematical Modelling Education in East and West, (2021); Journal of Theory and Practice in Education, (2017); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Mathematical Epistemology and Psychology, (1966); Journal of Mathematical Modelling and Application, (2009); Modelling and Applications in Mathematics Education, (2007); Mathematical Modelling Ictma 12 Education Engineering and Economics, (2007); Modelling Applications and Applied Problem Solving, (1989); Borromeo-Ferri, Rita, Theoretical and empirical differentiations of phases in the modelling process, ZDM - International Journal on Mathematics Education, 38, 2, pp. 86-95, (2006)F.K. Mumcu; Digitalization in Early Childhood Education, Department of Education Research and Teacher Education, University of Graz, Graz, Austria; email: filiz.mumcu@uni-graz.atNaNElsevier LtdNaNNaNNaNNaNNaN18711871NaNNaNNaNEnglishThink. Skills Creat.ArticleFinalAll Open Access; Hybrid Gold Open AccessScopus2-s2.0-10502123810810.1016/j.tsc.2025.102049
4van Bergen, R.; Huebotter, J.; A.; Lanillos, P.van Bergen, Ruben S. (55502596000); Huebotter, Justus F. (57901993200); Lanillos, Pablo (24076529300)55502596000; 57901993200; 60247114700; 24076529300Object-centric proto-symbolic behavioural reasoning from pixels2026Neural Networks197NaN108407NaNNaN010.1016/j.neunet.2025.108407https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025196185&doi=10.1016%2Fj.neunet.2025.108407&partnerID=40&md5=1371a88426221f67f7792f02362b7a13Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spainvan Bergen, Ruben S., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Huebotter, Justus F., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; null, null, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain; Lanillos, Pablo, Radboud Universiteit, Nijmegen, Gelderland, Netherlands, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, SpainAutonomous intelligent agents must bridge computational challenges at disparate levels of abstraction, from the low-level spaces of sensory input and motor commands to the high-level domain of abstract reasoning and planning. A key question in designing such agents is how best to instantiate the representational space that will interface between these two levels—ideally without requiring supervision in the form of expensive data annotations. These objectives can be efficiently achieved by representing the world in terms of objects (grounded in perception and action). In this work, we present a novel, brain-inspired, deep-learning architecture that learns from pixels to interpret, control, and reason about its environment, using object-centric representations. We show the utility of our approach through tasks in synthetic environments that require a combination of (high-level) logical reasoning and (low-level) continuous control. Results show that the agent can learn emergent conditional behavioural reasoning, such as (A → B)∧(¬A → C), as well as logical composition (A → B)∧(A → C)⊢A → (B∧C) and XOR operations, and successfully controls its environment to satisfy objectives deduced from these logical rules. The agent can adapt online to unexpected changes in its environment and is robust to mild violations of its world model, thanks to dynamic internal desired goal generation. While the present results are limited to synthetic settings (2D and 3D activated versions of dSprites), which fall short of real-world levels of complexity, the proposed architecture shows how to manipulate grounded object representations, as a key inductive bias for unsupervised learning, to enable behavioral reasoning. © 2025 The Author(s)Brain-inspired perception and control; Deep learning architectures; Object-centric reasoningAbstracting; Architecture; Behavioral research; Deep learning; Intelligent agents; Memory architecture; Unsupervised learning; Autonomous Intelligent Agents; Behavioral reasoning; Brain-inspired; Brain-inspired perception and control; Computational challenges; Deep learning architecture; Learn+; Learning architectures; Object-centric reasoning; Sensory motors; Autonomous agents; abstract thinking; article; clinical article; controlled study; deep learning; human; learning; logical reasoning; reasoning; sensory stimulationNaNNaNNaNNaNNaNNaNundefined, (2022); Iclr2022 Workshop on the Elements of Reasoning Objects Structure and Causality, (2022); undefined, (2025); Battaglia, Peter W., Interaction networks for learning about objects, relations and physics, Advances in Neural Information Processing Systems, pp. 4509-4517, (2016); Battaglia, Peter W., Simulation as an engine of physical scene understanding, Proceedings of the National Academy of Sciences of the United States of America, 110, 45, pp. 18327-18332, (2013); van Bergen, Ruben S., Object-Based Active Inference, Communications in Computer and Information Science, 1721 CCIS, pp. 50-64, (2023); Bas, Fred, Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind, Proceedings - IEEE International Conference on Robotics and Automation, 2022-January, pp. 5389-5395, (2022); Cowley, Stephen John, How human infants deal with symbol grounding, Interaction Studies, 8, 1, pp. 83-104, (2007); undefined, (2022); Driess, Danny, Learning Multi-Object Dynamics with Compositional Neural Radiance Fields, Proceedings of Machine Learning Research, 205, pp. 1755-1768, (2023)P. Lanillos; Donders Institute, Radboud University, Nijmegen, Netherlands; email: p.lanillos@csic.esNaNElsevier LtdNaNNaNNaNNaNNaN08936080NaNNNETENaNEnglishNeural Netw.ArticleFinalAll Open Access; Hybrid Gold Open AccessScopus2-s2.0-10502519618510.1016/j.neunet.2025.108407
5Hristov, M.; Yada, T.; Fagerlund, J.; Näykki, P.; Häkkinen, P.Hristov, Mitcho (60244741200); Yada, Takumi (57211492229); Fagerlund, Janne (57204184281); Näykki, Piia (24344723200); Häkkinen, Päivi H. (55917698700)60244741200; 57211492229; 57204184281; 24344723200; 55917698700Understanding the relationships among ICT use, self-efficacy, and achievement in PISA 2022: A multigroup analysis featuring gender and immigrant status2026Computers and Education244NaN105539NaNNaN010.1016/j.compedu.2025.105539https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025142999&doi=10.1016%2Fj.compedu.2025.105539&partnerID=40&md5=aababda657dab00c082b3c70c04fd383University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Faculty of Education and Psychology, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Department of Teacher Education, University of Jyväskylä, Jyvaskyla, Central Finland, FinlandHristov, Mitcho, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Yada, Takumi, University of Jyväskylä, Jyvaskyla, Central Finland, Finland, Faculty of Education and Psychology, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Fagerlund, Janne, Department of Teacher Education, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Näykki, Piia, Department of Teacher Education, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Häkkinen, Päivi H., University of Jyväskylä, Jyvaskyla, Central Finland, FinlandThis study investigates the relationships among students’ use of ICT for learning and leisure, self-efficacy in digital competencies, and achievement in math, reading, and science, and compares differences based on gender and immigrant background. Previous studies show inconsistent relationships among these variables. Student background affects the use of ICT, and while self-efficacy may vary depending on the subject, it has had positive effects on academic achievement. In this study, self-efficacy in digital competencies is viewed as two different competence beliefs: computer and information literacy (CIL) and computational thinking (CT). Although self-efficacy in CIL and CT has had varying effects on digital skills, the effects on math, reading, and science are not well studied. We analyzed Finnish data (N = 10,239) from PISA 2022 using general and multigroup structural equation modelling. We found that ICT use for learning had little to no practical significance across groups. ICT use for leisure and self-efficacy in CT were associated with being approximately a year or more behind in math, reading, and science across groups. Self-efficacy in CIL was associated with being two or more years ahead and played a protective short-term role as a mediator, especially in immigrants. These findings imply that closer integration of ICT use for learning with subject-specific goals in authentic learning contexts may promote their contribution to achievement. Further research should examine how different uses of ICT and CT skill development interact with subject learning in practice and how schools can be supported in adopting more pedagogically purposeful digital activities. © 2025 The AuthorsAcademic achievement; Computational thinking; Computer and information literacy; Digital competencies; ICT use; Mediation effects; Self-efficacyComputational methods; E-learning; Teaching; Academic achievements; Computational thinkings; Computer literacy; Digital competency; Digital skills; ICT use; Information literacy; Mediation effect; Multi-group; Self efficacy; StudentsNaNNaNNaNNaNNaNNaNBandura, Albert, Perceived Self-Efficacy in Cognitive Development and Functioning, Educational Psychologist, 28, 2, pp. 117-148, (1993); Bhutoria, Aditi, Patterns of cognitive returns to Information and Communication Technology (ICT) use of 15-year-olds: Global evidence from a Hierarchical Linear Modeling approach using PISA 2018, Computers and Education, 181, (2022); Caeli, Elisa Nadire, ICT Use, Self-efficacy, and the Future of Eighth-Grade Students: a Qualitative Study of Gender Differences, TechTrends, 69, 1, pp. 233-243, (2025); Campos, Diego G., Digital gender gaps in Students’ knowledge, attitudes and skills: an integrative data analysis across 32 Countries, Education and Information Technologies, 29, 1, pp. 655-693, (2024); Chen, Fangfang, Sensitivity of goodness of fit indexes to lack of measurement invariance, Structural Equation Modeling, 14, 3, pp. 464-504, (2007); Courtney, Matthew G.R., The influence of ict use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s pisa surveys, Large-Scale Assessments in Education, 10, 1, (2022); 2nd Survey of Schools ICT in Education Objective 1 Benchmark Progress in ICT in Schools, (2019); Faber, Janke M., The effects of a digital formative assessment tool on mathematics achievement and student motivation: Results of a randomized experiment, Computers and Education, 106, pp. 83-96, (2017); Koulutuksen Tutkimuslaitos Finnish Institute for Educational Research, (2024)M. Hristov; Finnish Institute for Educational Research, University of Jyväskylä, Jyväskylä, Finland; email: mitcho.a.hristov@jyu.fiNaNElsevier LtdNaNNaNNaNNaNNaN03601315NaNCOMEDNaNEnglishComput EducArticleFinalAll Open Access; Hybrid Gold Open AccessScopus2-s2.0-10502514299910.1016/j.compedu.2025.105539
6Kurikawa, T.; Kaneko, K.Kurikawa, Tomoki (36677377200); Kaneko, Kunihiko (7403696146)36677377200; 7403696146Stability control of metastable states as a unified mechanism for flexible temporal modulation in cognitive processing2026Neural Networks196NaN108381NaNNaN010.1016/j.neunet.2025.108381https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024706206&doi=10.1016%2Fj.neunet.2025.108381&partnerID=40&md5=cf1a6d80c6651eb4019a383a29b31aaaDepartment of Complex and Intelligent Systems, Future University - Hakodate, Hakodate, Hokkaido, Japan; Niels Bohr Institutet, Copenhagen, Hovedstaden, Denmark; Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, JapanKurikawa, Tomoki, Department of Complex and Intelligent Systems, Future University - Hakodate, Hakodate, Hokkaido, Japan; Kaneko, Kunihiko, Niels Bohr Institutet, Copenhagen, Hovedstaden, Denmark, Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, JapanFlexible modulation of temporal dynamics in neural sequences underlies many cognitive processes. For instance, we can adaptively change the speed of motor sequences and speech. While such flexibility is influenced by various factors such as attention and context, the common neural mechanisms responsible for this modulation remain poorly understood. We developed a biologically plausible neural network model that incorporates neurons with multiple timescales and Hebbian learning rules. This model is capable of generating simple sequential patterns as well as performing delayed match-to-sample (DMS) tasks that require the retention of stimulus identity. Fast neural dynamics establish metastable states, while slow neural dynamics maintain task-relevant information and modulate the stability of these states to enable temporal processing. We systematically analyzed how factors such as neuronal gain, external input strength (contextual cues), and task difficulty influence the temporal properties of neural activity sequences-specifically, dwell time within patterns and transition times between successive patterns. We found that these factors flexibly modulate the stability of metastable states. Our findings provide a unified mechanism for understanding various forms of temporal modulation and suggest a novel computational role for neural timescale diversity in dynamically adapting cognitive performance to changing environmental demands. Author Summary: The brain often uses sequences of neural activity to perform complex cognitive tasks such as recognizing speech, making decisions, or holding information in working memory. These sequences can speed up or slow down depending on factors like attention, task difficulty, or expectations-but how the brain controls this timing remains unclear. In this study, we built a biologically plausible model of a neural network that includes both fast and slow neurons and learns tasks through simple, realistic rules. We show that the slow neurons can hold onto past information and control how long the network activity stays in each state of a neural sequence. This control depends on the stability of each state, which is influenced by factors such as the external input strength, task difficulty, and top-down modulation. Our model coherently explains a variety of experimental findings and provides a unified theory for how the brain might flexibly adjust the speed of thought by taking advantage of diverse timescales of neural activity. © 2025 Elsevier LtdMetastable state; Multiple neural timescales; Sequential patterns; Speed modulation; Temporal scaling; Working memoryBrain; Cognitive systems; Dynamics; Neural networks; Neurons; Stability; Meta-stable state; Multiple neural timescale; Neural activity; Sequential patterns; Speed modulation; Task difficulty; Temporal modulations; Temporal scaling; Time-scales; Working memory; Computation theory; article; artificial neural network; cognition; controlled study; dwell time; human experiment; learning; mental performance; nerve cell network; nonhuman; speech; thinking; velocity; working memoryNaNNaNNaNNaNNaNNaNAmari, Shunichi, Learning patterns and pattern sequences by self-organizing nets of threshold elements, IEEE Transactions on Computers, C-21, 11, pp. 1197-1206, (1972); Beiran, Manuel, Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks, PLOS Computational Biology, 15, 3, (2019); Benozzo, Danilo, Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task, Cell Reports, 35, 1, (2021); Bernacchia, Alberto, A reservoir of time constants for memory traces in cortical neurons, Nature Neuroscience, 14, 3, pp. 366-372, (2011); Boerlin, Martin, Predictive Coding of Dynamical Variables in Balanced Spiking Networks, PLOS Computational Biology, 9, 11, (2013); Bollimunta, Anil, Neural dynamics of choice: Single-trial analysis of decision-related activity in parietal cortex, Journal of Neuroscience, 32, 37, pp. 12684-12701, (2012); Cavanagh, Sean Edward, A Diversity of Intrinsic Timescales Underlie Neural Computations, Frontiers in Neural Circuits, 14, (2020); Cavanagh, Sean Edward, Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex, Nature Communications, 9, 1, (2018); Cavanagh, Sean Edward, Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice, eLife, 5, OCTOBER2016, (2016); Chalk, Matthew, Neural oscillations as a signature of efficient coding in the presence of synaptic delays, eLife, 5, 2016JULY, (2016)T. Kurikawa; Department of Complex and Intelligent Systems, Future University Hakodate, Hakodate Hokkaido, 116-2 Kamedanakano-cho, 041-8655, Japan; email: kurikawa@fun.ac.jpNaNElsevier LtdNaNNaNNaNNaNNaN08936080NaNNNETENaNEnglishNeural Netw.ArticleFinalNaNScopus2-s2.0-10502470620610.1016/j.neunet.2025.108381
7Jing, P.; Lee, K.; Zhang, Z.; Zhou, H.; Yuan, Z.; Gao, Z.; Zhu, L.; Papanastasiou, G.; Fang, Y.; Yang, G.Jing, Peiyuan (59706029600); Lee, Kinhei (59145910100); Zhang, Zhenxuan (58928546300); Zhou, Huichi (58899029300); Yuan, Zhengqing (59057862700); Gao, Zhifan (55320405200); Zhu, Lei (56399719900); Papanastasiou, Giorgos (56539707000); Fang, Yingying (57204847825); Yang, Guang (57216243504)59706029600; 59145910100; 58928546300; 58899029300; 59057862700; 55320405200; 56399719900; 56539707000; 57204847825; 57216243504Reason like a radiologist: Chain-of-thought and reinforcement learning for verifiable report generation2026Medical Image Analysis109NaN103910NaNNaN010.1016/j.media.2025.103910https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024997351&doi=10.1016%2Fj.media.2025.103910&partnerID=40&md5=34be87236b93a12211a3c7bff4c3ffb0Imperial College London, London, United Kingdom; College of Engineering, Notre Dame, IN, United States; School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, Guangdong, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China; Mathematics Research Centre, Academy of Athens, Athens, Attica, Greece; Archimedes Unit, Athena Research Centre, Athens, Greece; National Heart and Lung Institute, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United KingdomJing, Peiyuan, Imperial College London, London, United Kingdom; Lee, Kinhei, Imperial College London, London, United Kingdom; Zhang, Zhenxuan, Imperial College London, London, United Kingdom; Zhou, Huichi, Imperial College London, London, United Kingdom; Yuan, Zhengqing, College of Engineering, Notre Dame, IN, United States; Gao, Zhifan, School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, Guangdong, China; Zhu, Lei, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China; Papanastasiou, Giorgos, Mathematics Research Centre, Academy of Athens, Athens, Attica, Greece, Archimedes Unit, Athena Research Centre, Athens, Greece; Fang, Yingying, Imperial College London, London, United Kingdom, National Heart and Lung Institute, London, United Kingdom; Yang, Guang, Imperial College London, London, United Kingdom, National Heart and Lung Institute, London, United Kingdom, Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United KingdomRadiology report generation is critical for efficiency, but current models often lack the structured reasoning of experts and the ability to explicitly ground findings in anatomical evidence, which limits clinical trust and explainability. This paper introduces BoxMed-RL, a unified training framework to generate spatially verifiable and explainable chest X-ray reports. BoxMed-RL advances chest X-ray report generation through two integrated phases: (1) Pretraining Phase. BoxMed-RL learns radiologist-like reasoning through medical concept learning and enforces spatial grounding with reinforcement learning. (2) Downstream Adapter Phase. Pretrained weights are frozen while a lightweight adapter ensures fluency and clinical credibility. Experiments on two widely used public benchmarks (MIMIC-CXR and IU X-Ray) demonstrate that BoxMed-RL achieves an average 7 % improvement in both METEOR and ROUGE-L metrics compared to state-of-the-art methods. An average 5 % improvement in large language model-based metrics further underscores BoxMed-RL’s robustness in generating high-quality reports. Related code and training templates are publicly available at https://github.com/ayanglab/BoxMed-RL . © 2025 The Author(s).Explainability; Radiology report generation; Reinforcement learningComputational methods; Machine learning; Current modeling; Explainability; Learn+; Phase 1; Pre-training; Radiology report generation; Radiology reports; Reinforcement learnings; Report generation; Training framework; Radiology; article; benchmarking; human; large language model; radiologist; reasoning; thinking; thorax radiography; X ray; X ray analysisNaNNaNNaNNaNNaNNaNGpt 4 Technical Report, (2023); Bigolin Lanfredi, Ricardo, REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays, Scientific Data, 9, 1, (2022); Boecking, Benedikt, Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing, Lecture Notes in Computer Science, 13696 LNCS, pp. 1-21, (2022); Brady, Adrian Paul, Discrepancy and error in radiology: Concepts, causes and consequences, Ulster Medical Journal, 81, 1, pp. 3-9, (2012); Chexpert Plus Augmenting A Large Chest X Ray Dataset with Text Radiology Reports Patient Demographics and Additional Image Formats, (2024); Chen, Zhihong, Cross-modal memory networks for radiology report generation, 1, pp. 5904-5914, (2021); Chen, Zhihong, Generating radiology reports via memory-driven transformer, pp. 1439-1449, (2020); Chen, Zhe, Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 24185-24198, (2024); Forty Second International Conference on Machine Learning, (2025); Comparative Interpretation of CT and Standard Radiography of the Chest, (2011)G. Yang; Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, United Kingdom; email: g.yang@imperial.ac.ukNaNElsevier B.V.NaNNaNNaNNaNNaN13618415NaNMIAECNaNEnglishMed. Image Anal.ArticleFinalAll Open Access; Hybrid Gold Open AccessScopus2-s2.0-10502499735110.1016/j.media.2025.103910
8Ahmad, M.; Y.; Moreno-Benito, M.; S.; Rao, H.N.; Mustakis, J.; Karimi, I.A.Ahmad, Maaz (57216171666); Moreno-Benito, Marta (36782636600); Rao, Harsha Nagesh (59544492700); Mustakis, Jason G. (26536355200); Karimi, Iftekar Abubakar (7005509050)57216171666; 60238154100; 36782636600; 60238125600; 59544492700; 26536355200; 7005509050Cluster-based adaptive sampling methodology for systems modeling2026Computers and Chemical Engineering206NaN109527NaNNaN010.1016/j.compchemeng.2025.109527https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024757523&doi=10.1016%2Fj.compchemeng.2025.109527&partnerID=40&md5=08f4a3dd38ef3c45e5b8118556ebcb9bDepartment of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore; Worldwide Research and Development, Pfizer Inc., New York, NY, United States; Worldwide Research and Development, Pfizer Inc., Chennai, India; Pfizer Asia Manufacturing Pte Ltd., Singapore City, Singapore; Worldwide Research and Development, Pfizer Inc., New York, NY, United StatesAhmad, Maaz, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore; null, null, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore; Moreno-Benito, Marta, Worldwide Research and Development, Pfizer Inc., New York, NY, United States; null, null, Worldwide Research and Development, Pfizer Inc., Chennai, India; Rao, Harsha Nagesh, Pfizer Asia Manufacturing Pte Ltd., Singapore City, Singapore; Mustakis, Jason G., Worldwide Research and Development, Pfizer Inc., New York, NY, United States; Karimi, Iftekar Abubakar, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, SingaporeModeling real-world (experimental) or simulated (computational) systems using data-driven surrogate models involves selecting a sampling technique to generate the input-output data for training and selecting a surrogate form. In this work, we present a novel sampling technique, Cluster-based Adaptive Sampling, that generates training data smartly and adaptively for developing surrogate models over a given input domain. CAS iteratively clusters sampled points, defines Voronoi tessellation of cluster centroids, and approximates the tessellations using simple hypercubes. It then searches locally and globally over the domain at each iteration to identify nonlinear and under-explored regions respectively, where it samples two new points using a distance-based metric. CAS is agnostic to surrogate form and terminates automatically based on a surrogate quality metric. We assessed CAS against two existing sampling techniques on 40 diverse test functions using six surrogate forms. CAS outperformed both techniques in developing more accurate surrogates for a given computational effort and required lower computational effort for a specified accuracy across most test functions and forms. We highlight the practical applicability of CAS in modeling two pharmaceutical processes and showcase its superior performance over the two techniques. © 2025Active learning; Adaptive sampling; Design of experiments; Machine learning; Surrogate modeling; Systems modelingArtificial intelligence; Fuel additives; Input output programs; Iterative methods; Learning systems; Systems analysis; Systems thinking; Test facilities; Active Learning; Adaptive sampling; Cluster-based; Computational effort; Machine-learning; Real-world; Sampling technique; Surrogate modeling; System models; Test-functions; Design of experimentsNaNNaNNaNNaNNaNNaNAhmad, Maaz, Families of similar surrogate forms based on predictive accuracy and model complexity, Computers and Chemical Engineering, 163, (2022); Ahmad, Maaz, Surrogate Classification based on Accuracy and Complexity, Computer Aided Chemical Engineering, 49, pp. 1735-1740, (2022); Ahmad, Maaz, Revised learning based evolutionary assistive paradigm for surrogate selection (LEAPS2v2), Computers and Chemical Engineering, 152, (2021); Aute, Vikrant C., Cross-validation based single response adaptive design of experiments for Kriging metamodeling of deterministic computer simulations, Structural and Multidisciplinary Optimization, 48, 3, pp. 581-605, (2013); Bhakte, Abhijit, Alarm-based explanations of process monitoring results from deep neural networks, Computers and Chemical Engineering, 179, (2023); Bhosekar, Atharv, Advances in surrogate based modeling, feasibility analysis, and optimization: A review, Computers and Chemical Engineering, 108, pp. 250-267, (2018); Boukouvala, Fani, ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, Optimization Letters, 11, 5, pp. 895-913, (2017); Boukouvala, Fani, Feasibility analysis of black-box processes using an adaptive sampling Kriging-based method, Computers and Chemical Engineering, 36, 1, pp. 358-368, (2012); Boukouvala, Fani, Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO, European Journal of Operational Research, 252, 3, pp. 701-727, (2016); Boukouvala, Fani, Dynamic data-driven modeling of pharmaceutical processes, Industrial and Engineering Chemistry Research, 50, 11, pp. 6743-6754, (2011)I.A. Karimi; Department of Chemical & Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore; email: cheiak@nus.edu.sgNaNElsevier LtdNaNNaNNaNNaNNaN00981354008030270X; 9780080302706CCENDNaNEnglishComput. Chem. Eng.ArticleFinalNaNScopus2-s2.0-10502475752310.1016/j.compchemeng.2025.109527
9Jha, N.K.; Tsai, M.-J.Jha, Nitesh Kumar (58658236100); Tsai, Meng Jung (7403551418)58658236100; 7403551418Using machine learning approaches to predict Taiwanese eighth graders' computational thinking performance in ICILS 2023 study2026Computers in Human Behavior Reports21NaN100896NaNNaN010.1016/j.chbr.2025.100896https://www.scopus.com/inward/record.uri?eid=2-s2.0-105023962868&doi=10.1016%2Fj.chbr.2025.100896&partnerID=40&md5=575d8689eec33907c5983d1a39af339cProgram of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan; Program of Learning Sciences, National Taiwan Normal University, Taipei, TaiwanJha, Nitesh Kumar, Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan; Tsai, Meng Jung, Program of Learning Sciences, National Taiwan Normal University, Taipei, TaiwanThis study employs machine learning approaches to examine how socio-demographic, student-related, and school-related variables predict the computational thinking (CT) performance of 5211 Taiwanese eighth graders in the ICILS 2023 study (Fraillon, 2024). It further aims to identify the key predictors of Taiwanese students' CT scores in this international evaluation project. The study used seven trained models: Multinomial Logistic Regression, Random Forest, AdaBoost, XGBoost, LightGBM, Gradient Boosting classifier, and Stacking Ensemble to identify and rank the variables that affect CT scores. The CT performance score was used as a binary variable with two classes: below and above average score. Findings showed that XGBoost and Stacking Ensemble performed best when classifying below and average CT scores respectively in terms of precision, recall and F1 score. In addition, among the variables, student-related variables had the highest impact on students' CT skills followed by school-related and socio-demographic. Among student-related variables, CT disposition was the most significant variable followed by ICT self-efficacy and academic multitasking. Further, among school-related factor, learning special applications in class had significant impact followed by a low impact of socio-demographic variables such as home literacy and parents' education. This study offers practical implications for educators, policymakers, and curriculum designers by underscoring the role of CT disposition and recommending targeted support for enhancing students’ digital self-efficacy. Additionally, the study shows the potential of ML for creating adaptive learning environments and guiding data-informed decisions in educational policy and practice. © 2025 The Authors.21st century abilities; Applications in subject areas; Data science applications in education; Information literacy; Secondary educationNaNNaNNaNNaNNaNNational Taiwan Normal University, NTNU; International Association for the Evaluation of Educational Achievement, IEA; National Science and Technology Council, NSTC; Ministry of Education, MOEThe authors thank to the financial support from The National Science and Technology Council, Taiwan, and The Ministry of Education, Taiwan. Special thanks go to the colleagues from the IEA in Hamberg, Germany, the IEA in Amsterdam, Neitherlands, and the ICILS 2023 National Research Center at NTNU, Taiwan for their excellent research collaboration in the ICILS 2023 study.Akiba, Takuya, Optuna: A Next-generation Hyperparameter Optimization Framework, pp. 2623-2631, (2019); Alhassan, Amal, Predict students' academic performance based on their assessment grades and online activity data, International Journal of Advanced Computer Science and Applications, 11, 4, (2020); Allen, Jeff, Third-year college retention and transfer: Effects of academic performance, motivation, and social connectedness, Research in Higher Education, 49, 7, pp. 647-664, (2008); Asselman, Amal, Enhancing the prediction of student performance based on the machine learning XGBoost algorithm, Interactive Learning Environments, 31, 6, pp. 3360-3379, (2023); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); International Journal of Educational Methodology, (2020); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Bradley, Janae, Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation, BMC Veterinary Research, 17, 1, (2021); Bradley, Janae, Developing predictive models for early detection of intervertebral disc degeneration risk, Healthcare Analytics, 2, (2022); Annual American Educational Research Association Meeting, (2012)M.-J. Tsai; Program of Learning Sciences, School of Learning Informatics, Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei, 162, Sec. 1, Hoping E. Rd., 106, Taiwan; email: mjtsai99@ntnu.edu.twNaNElsevier B.V.NaNNaNNaNNaNNaNNaNNaNNaNNaNEnglishComput. Hum. Behav. Rep.ArticleFinalAll Open Access; Gold Open AccessScopus2-s2.0-10502396286810.1016/j.chbr.2025.100896
AuthorsAuthor full namesAuthor(s) IDTitleYearSource titleVolumeIssueArt. No.Page startPage endCited byDOILinkAffiliationsAuthors with affiliationsAbstractAuthor KeywordsIndex KeywordsMolecular Sequence NumbersChemicals/CASTradenamesManufacturersFunding DetailsFunding TextsReferencesCorrespondence AddressEditorsPublisherSponsorsConference nameConference dateConference locationConference codeISSNISBNCODENPubMed IDLanguage of Original DocumentAbbreviated Source TitleDocument TypePublication StageOpen AccessSourceEIDdoi_norm
12182Wheatley, G.H.Wheatley, Grayson H. (36864672400)36864672400A Mathematics Curriculum for the Gifted and Talented1983Gifted Child Quarterly272NaN7780410.1177/001698628302700205https://www.scopus.com/inward/record.uri?eid=2-s2.0-67650426074&doi=10.1177%2F001698628302700205&partnerID=40&md5=2eb3af9cd5b078c190add8819d87d99bPurdue University, West Lafayette, IN, United StatesWheatley, Grayson H., Purdue University, West Lafayette, IN, United StatesDeveloping a mathematics program for the gifted is more than setting a faster pace through existing textbooks. It is important to step back and take a broad view of the problem. What type of thinking do we want to encourage? What do we want children to know? What modes of instruction are appropriate? This paper has outlined ten major strands in elementary school mathematics. The mathematics needs of the eighties and nineties will be different from the fifties and sixties. We must plan for the future. Certainly the gifted should be encouraged to reason and relate ideas. Problem solving is an excellent tool for this purpose. Computers are rapidly becoming a standard tool for thought and work. The gifted must learn to use them and this should include writing computer programs. With increased use of computers and calculators it is important that estimation skills be strong. Additionally, there is the necessity for acquiring concepts, principles, facts, and mathematical rules. We must strive to achieve the proper balance between computational skill and higher level thinking; both are important. A major thesis of this paper is that present textbooks do not develop higher level reasoning but over-emphasize computational rules. The ten strands described can form the basis for a balanced mathematics program for the gifted. © 1983, Sage Publications. All rights reserved.NaNNaNNaNNaNNaNNaNNaNNaNBogen, Joseph E., The other side of the brain. II. An appositional mind., Bulletin of the Los Angeles neurological societies, 34, 3, pp. 135-162, (1969); Teaching Problem Solving What Why and how, (1982); Saber Tooth Curriculum, (2025); Guide to Using Estimation Skills and Strategies, (1983); Journal for Research in Mathematics Education, (1978); Calculator Use and Problem Solving Strategies of Grade Six Pupils Final Report, (1982)NaNNaNNaNNaNNaNNaNNaNNaN00169862NaNNaNNaNEnglishGifted Child Q.ArticleFinalNaNScopus2-s2.0-6765042607410.1177/001698628302700205
12183Osborn, H.H.Osborn, Herbert H. (57017110100)57017110100The Assessment of Mathematical Abilities1983Educational Research251NaN2840210.1080/0013188830250104https://www.scopus.com/inward/record.uri?eid=2-s2.0-2842594331&doi=10.1080%2F0013188830250104&partnerID=40&md5=5a523c1b1e34666d7f2eec7f345fe485Goldsmiths, University of London, London, United KingdomOsborn, Herbert H., Goldsmiths, University of London, London, United KingdomExperience has suggested that the common,generalized assessment of mathematical ability by a single grade in a mathematics examination is inadequate.On the hypothesis that the thinking involved in mathematical activity may be resolved into four distinct,but not discreet, components: computational operations, pattern recognition, logical reasoning and the symbolic manipulation of abstract quantities (components C, P, L and 5), a test was devised and given to 322 pupils in fifth forms of seven secondary schools in the London and near-London area, a few months prior to their taking either the GCE O-level or CSE examinations in mathematics. From the scores gained in the test a profile was obtained for each pupil tested. A comparison of these profiles and of the vectors obtained from them with the grades gained in the examinations taken has enabled some important, albeit tentative, observations to be made on the structure of mathematics examinations and the processes of teaching the subject in schools. © 1983, Taylor & Francis Group, LLC. All rights reserved.NaNNaNNaNNaNNaNNaNNaNNaNStructure of Human Abilities, (1950); London University of London Press, (1960); Chicago University of Chicago Press, (1976); British Journal of Educational Psychology, (1977); Williams, John D., Teaching Emphases In Primary Mathematics, Educational Research, 14, 3, pp. 177-181, (1972); Wood, R., Objectives in the teaching of mathematics, Educational Research, 10, 2, pp. 83-98, (1968)NaNNaNNaNNaNNaNNaNNaNNaN00131881NaNNaNNaNEnglishEduc. Res.ArticleFinalNaNScopus2-s2.0-284259433110.1080/0013188830250104
12184Knapp, M.S.Knapp, Martin Sayers (7202388642)7202388642Computing, mathematics, and the nephrologist.1983Kidney International244NaN433435110.1038/ki.1983.178https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020837797&doi=10.1038%2Fki.1983.178&partnerID=40&md5=59431363cd7e6beeb648155a3b1ce585NaNKnapp, Martin Sayers,The potential of computing and mathematics to make major contributions to nephrology by making information retrieval and presentation more accurate, more complete, and more rapid and by providing immediate access to computational and graphic facilities is emphasized in this symposium issue. The realization that disordered physiology due to disease may not prevent the course of an illness being described in mathematical terms should encourage physicians, and others interested in pathophysiology, to integrate mathematics and statistics into their thinking and their practice.NaNarticle; biological model; computer; decision making; human; mathematics; medical record; nephrology; statistics; Computers; Decision Making; Humans; Mathematics; Medical Records; Models, Biological; Nephrology; Statistics; MLCS; MLOWNNaNNaNNaNNaNMedical Research Council, MRCThe concepts discussed in this introduction evolved when the author was Consultant Renal Physician to the Nottingham Hospitals, and in receipt of grants from the Medical Research Council and the Notting- ham & Nottinghamshire Kidney Fund.NaNNaNNaNNaNNaNNaNNaNNaNNaN00852538NaNNaN6645214.0EnglishKidney Int.ArticleFinalNaNScopus2-s2.0-002083779710.1038/ki.1983.178
12185Wilson Pearson, L.W.Wilson Pearson, L. (7103019064)7103019064Present thinking of the use of the singularity expansion in electromagnetic scattering computation1983Wave Motion54NaN355368710.1016/0165-2125(83)90022-7https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020804776&doi=10.1016%2F0165-2125%2883%2990022-7&partnerID=40&md5=2cf0c0ae0f1f3b693aba4a54e2af6705Department of Electrical Engineering, University of Mississippi, University, MS, United StatesWilson Pearson, L., Department of Electrical Engineering, University of Mississippi, University, MS, United StatesWhile the singularity expansion of electromagnetic scattering responses has received a great deal of attention over the last several years, a number of uncertainties have persisted in connection with its applicability and completeness. Recently, the dominant questions have been clarified, at least for one with pragmatic computational goals. This paper surveys the present understanding of the singularity expansion from the pragmatist's point-of-view. Attention is given to recent work which clarifies points which have been debated in the past. The interpretation of the expansion in the presence of a time-limited excitation function is discussed. Various means for determining the expansion parameters for a given object are surveyed. © 1983.NaNELECTROMAGNETIC WAVESNaNNaNNaNNaNOffice of Naval Research, ONR, (N00014-81-K-0256)This work was sponsored, in part, by the Office of Naval Research under Contract Number N00014-81-K-0256.Interaction Notes, (1971); Electromagnetics, (1981); Transient Electromagnetic Fields, (1976); Kennaugh, Edward M., The K-Pulse Concept, IEEE Transactions on Antennas and Propagation, 29, 2, pp. 327-331, (1981); Wilson Pearson, L., The Extraction of the Singularity Expansion Description of a Scatterer from Sampled Transient Surface Current Response, IEEE Transactions on Antennas and Propagation, 28, 2, pp. 182-190, (1980); Cho, K. S., Calculation of the SEM Parameters from the Transient Response of a Thin Wire, IEEE Transactions on Antennas and Propagation, 28, 6, (1980); Wilson Pearson, L., SEM Parameter Extraction Through Transient Surface Current Measurement Using King-Type Probes, IEEE Transactions on Antennas and Propagation, 30, 2, pp. 260-266, (1982); Journal De L Ecole Polytechnique De Paris, (1795); Applied Analysis, (1956); Marin, Lennart, Natural-Mode Representation of Transient Scattered Fields, IEEE Transactions on Antennas and Propagation, 21, 6, pp. 809-818, (1973)NaNNaNNaNNaNNaNNaNNaNNaN01652125NaNNaNNaNEnglishWave Mot.ArticleFinalNaNScopus2-s2.0-002080477610.1016/0165-2125(83)90022-7
12186Forsyth, R.A.; Ansley, T.N.Forsyth, Robert A. (57029929200); Ansley, Timothy N. (6505980402)57029929200; 6505980402The importance of computational skill for answering items in a mathematics problem solving test: Implications for construct validity1982Educational and Psychological Measurement421NaN257263110.1177/0013164482421032https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973850769&doi=10.1177%2F0013164482421032&partnerID=40&md5=b13e03d4b57537a03d6020ecd92b0791University of Iowa, Iowa City, IA, United StatesForsyth, Robert A., University of Iowa, Iowa City, IA, United States; Ansley, Timothy N., University of Iowa, Iowa City, IA, United StatesThe primary purpose of this study was to investigate the importance of computational skill for answering items in the Quantitative Thinking subtest (Test Q) of the Iowa Tests of Educational Development (ITED). Nine matched pairs of schools participated in the study. One school from each pair allowed students to use calculators when taking Test Q, while the other school did not allow calculators to be employed. The difficulty levels of the items in Test Q were calculated for both test conditions. In general, the differences in p values were very small. On the basis of these results, it was concluded that computational skill is not a major factor contributing to an examinee's score on Test Q and thus that the use of Test Q as a measure of problem solving ability is not compromised by its computational requirements. © 1982, Sage Publications. All rights reserved.NaNNaNNaNNaNNaNNaNNaNNaNIowa Tests of Educational Development Form X 8, (1993); Mathematics Teacher, (1978); Forsyth, Robert A., MEASURING PROBLEM SOLVING ABILITY IN MATHEMATICS WITH MULTIPLE‐CHOICE ITEMS: THE EFFECT OF ITEM FORMAT ON SELECTED ITEM AND TEST CHARACTERISTICS, Journal of Educational Measurement, 17, 1, pp. 31-43, (1980); Iowa Tests of Basic Skills, (1964); Arithmetic Teacher, (1977); Today S Education, (1977); Arithmetic Teacher, (1977)NaNNaNNaNNaNNaNNaNNaNNaN00131644NaNNaNNaNEnglishEduc. Psychol. Meas.ArticleFinalNaNScopus2-s2.0-8497385076910.1177/0013164482421032
12187Cohen, M.D.Cohen, Michael D. (57194515983)57194515983The power of parallel thinking1981Journal of Economic Behavior and Organization24NaN2853063510.1016/0167-2681(81)90011-1https://www.scopus.com/inward/record.uri?eid=2-s2.0-0000099394&doi=10.1016%2F0167-2681%2881%2990011-1&partnerID=40&md5=34e606f95840401d7b7cefc0db1706bbUniversity of Michigan, Ann Arbor, Ann Arbor, MI, United StatesCohen, Michael D., University of Michigan, Ann Arbor, Ann Arbor, MI, United StatesA small computer model demonstrates that an appropriate organization of boundedly rational individuals can find optimal policies in an environment that is overwhelmingly complex for unorganized decision makers. The model is also used to identify conditions under which optimal - or even good - policies are not found. The demonstrated adaptive power of the model is interpreted in light of recent developments in the theory of computational complexity that place new stress on powerful methods of search, and of new models from computer science which markedly advance search effectiveness by harnessing parallel structures of information processing. © 1981.NaNNaNNaNNaNNaNNaNNaNNaNOrganizations and Environments, (1979); Cognitive Psychology and Its Implications, (1990); Borosh, Itshak, Bounds on positive integral solutions of linear diophantine equations, Proceedings of the American Mathematical Society, 55, 2, pp. 299-304, (1976); Discussion Paper no 151, (1980); Discussion Paper no 153, (1982); Cook, Stephen A., The complexity of theorem-proving procedures, Proceedings of the Annual ACM Symposium on Theory of Computing, pp. 151-158, (1971); Bell Journal of Economics, (1980); Handbook of Learning and Cognitive Processes, (1975); Netl A System for Representing and Using Real World Knowledge, (1979); Computers and Intractability, (1979)NaNNaNNaNNaNNaNNaNNaNNaN01672681NaNJEBODNaNEnglishJ. Econ. Behav. Organ.ArticleFinalNaNScopus2-s2.0-000009939410.1016/0167-2681(81)90011-1
12188Fearnley-Sander, D.Fearnley-Sander, Desmond (6506443658)6506443658Learning to calculate and learning mathematics1980International Journal of Mathematical Education in Science and Technology111NaN111114010.1080/0020739800110117https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946291105&doi=10.1080%2F0020739800110117&partnerID=40&md5=b78435cdc181811acd652e2e1f95919dDepartment of Mathematics, University of Tasmania, Hobart, TS, AustraliaFearnley-Sander, Desmond, Department of Mathematics, University of Tasmania, Hobart, TS, AustraliaA calculator solution of a simple computational problem is discussed with emphasis on its ramifications for the understanding of some fundamental theorems of pure mathematics and techniques of computing. Today's mathematics teachers grew up under the influence of the formalist, structuralist tendencies which dominated thinking about mathematics between the nineteen thirties and the sixties. In the last decade or two, though, a new influence, constructive and algorithmic, has become increasingly important and it is now about to have an effect upon mathematical education in the schools. This is very healthy; for, whatever may be their relative importance in mathematics as a whole, it is clear that the algorithmic approach has much to say to the child which formalism leaves out. In a word, it shifts the emphasis from abstractions about sets and relations to concrete facts about something of direct and immediate importance to him: numbers. © Taylor & Francis Group, LLC.NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0020739XNaNNaNNaNEnglishInt. J. Math. Educ. Sci. Technol.ArticleFinalNaNScopus2-s2.0-8494629110510.1080/0020739800110117
12189Basco, D.R.Basco, David R. (7006364457)7006364457COMPUTATIONAL METHODS TO MODEL UNSTEADY VARIABLE DENSITY FLOWS IN HYDRAULIC DREDGING.1977NaNv1NaNJ4J40NaNhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0017432824&partnerID=40&md5=215bbcee4fca7f4827c77971b0ec1c67NaNBasco, David R.,All dredging processes involve unsteady flows. We can now model the complicated variations of slurry density, velocity and pressure with time in the piping system by the use of high speed computers and finite-difference techniques. This paper outlines how these computations can be accomplished and qualitatively discusses some ramifications on present thinking based on steady-state analysis.NaNMATHEMATICAL TECHNIQUES - Finite Difference Method; Dredging; consen1tion; dispersion; equation of state; expansion; finite differenc; hydraulic transport; mass; method; momentum; pipeline; pressure; transport; turbulentNaNNaNNaNNaNNaNNaNNaNNaNNaNBHRA (Br Hydromech Res Assoc) Fluid EngNaNPap presented at the Int Symp on Dredging Technol, 2ndNaNNaNNaNNaNNaNNaNNaNNaNNaNArticleFinalNaNScopus2-s2.0-0017432824NaN
12190Flaherty, E.G.Flaherty, E. G. (57011033900)57011033900The thinking aloud technique and problem solving ability1975Journal of Educational Research686NaN2232251810.1080/00220671.1975.10884753https://www.scopus.com/inward/record.uri?eid=2-s2.0-0010997242&doi=10.1080%2F00220671.1975.10884753&partnerID=40&md5=53ffb760abe254c9977031742360c604Nasson College, Springvale, ME, United StatesFlaherty, E. G., Nasson College, Springvale, ME, United StatesThe effects of overt verbalization and practice on problem solving ability were examined. The 100 secondary school students who served as Ss were divided into four groups: (1) those who received practice word problems and solved problems while thinking aloud, (2) those who did not practice but solved problems while thinking aloud, (3) those who practiced but dkl not verbalize, and (4) those who received no practice and did not verbalize. Analysis of variance revealed that neither overt verbalization nor practice significantly influenced problem solving scores. However, Ss who were required to think aloud made significantly more computational errors than those who worked without verbalizing. © Taylor & Francis.NaNNaNNaNNaNNaNNaNNaNNaNBroverman, Donald M., Individual differences in task performance under conditions of cognition interference, Journal of Personality, 26, 1, pp. 94-105, (1958); Brunk, Larry, A correlational study of two reasoning problems, Journal of Experimental Psychology, 55, 3, pp. 236-241, (1958); Problem Solving, (1966); Cognitive Processes Used in Solving Mathematical Problems Unpublished Doctoral Dissertation, (1973); Psychological Monographs, (1957); Psychological Reports, (1957); Principles of Psychology, (1890); Kilpatrick, Jeremy, 10: Problem Solving in Mathematics, Review of Educational Research, 39, 4, pp. 523-534, (1969); Problem Solving, (1966); Patrick, Catharine, Creative thought in artists, Journal of Psychology, 4, 1, pp. 35-73, (1937)NaNNaNNaNNaNNaNNaNNaNNaN00220671NaNNaNNaNEnglishJ. Educ. Res.ArticleFinalNaNScopus2-s2.0-001099724210.1080/00220671.1975.10884753
12191Wegner, P.Wegner, Peter (7005517088)7005517088Three Computer Cultures: Computer Technology, Computer Mathematics, and Computer Science1970Advances in Computers10CNaN7784010.1016/S0065-2458(08)60431-3https://www.scopus.com/inward/record.uri?eid=2-s2.0-41249083505&doi=10.1016%2FS0065-2458%2808%2960431-3&partnerID=40&md5=ab66e09c65ad8c54442de0c0f04ba621Department of Computer Science, Providence, RI, United StatesWegner, Peter, Department of Computer Science, Providence, RI, United StatesAs scientific and technological tools, computers have proved so useful that computer science is widely regarded as a technological discipline whose purpose is to create problem-solving tools for other disciplines. Within computer science there is a group of theoreticians who build mathematical models of computational processes. Yet computer science is neither a branch of technology nor a branch of mathematics. It involves a new way of thinking about computational schemes that is partly technological and partly mathematical but contains a unique ingredient that differs qualitatively from those of traditional disciplines. This chapter illustrates the special quality that distinguishes computer science from technology and mathematics by the means of examples from the emerging theory of programming languages. The computer revolution is comparable to the industrial revolution. Just as machines have reduced the physical drudgery of man, computers are reducing his mental drudgery. The central role played by ‘energy’ in the industrial revolution is replaced in the computer revolution by ‘information.’ This chapter focuses on technological and scientific programming languages and mathematical models related to computers. © 1970, Academic Press Inc.NaNNaNNaNNaNNaNNaNNational Science Foundation, NSF, (GP7347); National Science Foundation, NSFapproach to computer science, while the characterization of languages and systems constitutes a “top-down” approach. The prosent paper is concerned principally with “top-down” computer science. A descriptive model for broad classes of computation is developed, and a number of questions associated with the modeling of programming languages are discussed in some detail to show that there are problems in computer science that are neither mathematical nor technological. This work was supported in part by NSF grant GP7347.Theories of Abstract Automata, (1969); Information Theory, (1965); Computer Journal, (1963); Computer J, (1969); Handbook of Mathematical Psychology, (1963); Proc Extensible Languages Symp SIGPLAN Notices, (1969); Annals of Mathematics Studies, (1941); External Specifications for A Common Business Oriented Language, (1962); Universal Algebra, (1965); Combinatory Logic, (1958)NaNNaNNaNNaNNaNNaNNaNNaN006524580123737478; 0120121662; 9780128138526; 9780323910897; 0120121670; 012373746X; 9780128171578; 9780123737465; 9780323898102; 9780128137864NaNNaNEnglishAdv. Comput.ArticleFinalNaNScopus2-s2.0-4124908350510.1016/s0065-2458(08)60431-3